Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM

This paper aims to explore deep learning model to learn short-term and long-term patterns from imbalanced input dataset. Data for this study are imbalanced card transactions from an Indonesia bank in period 2016–2017 with binary labels (nonfraud or fraud). From 50 features of the dataset, 30 principal components of data contribute to 87 % of the cumulative Eigenvalues. This study explores the effect of nonfraud to fraud sample ratio from 1 to 4 and three models: Convolutional Neural Network (CNN), Stacked Long Short-term Memory (SLSTM), and Hybrid of CNN-LSTM. Using Area Under the ROC Curve (AUC) as model performance, CNN achieved the highest AUC for R=1,2,3,4 followed by SLSTM and CNN-LSTM.

Intelligent Traffic Monitoring System (ITMS) for Smart City Based on IoT Monitoring

The internet network is the essential thing in life today, almost all devices are connected to the internet network, and many have been implemented in virtually all areas of life that exist in society today, with the concept of smart city internet system very, very play the most crucial role. This is because all have been connected to the internet network, and this system is expected to reduce many of the problems in the developing cities or developed cities. With an excellent precautionary method will lead to an orderly community system that passes traffic, can be monitored regarding vehicles, highways and traffic signs. Moreover, with intelligent monitoring, many help the government and officers work, with proper tracking the community can measure the distance traveled so that they can arrive quickly at the destination, and reduce accident in the road. The proposed Internet of Thing (IoT) monitoring which applied such as motion sensor monitoring, ultrasonic sensor monitoring, Passive Infra Red (PIR) sensor monitoring and speed sensor monitoring.

Software metrics for fault prediction using machine learning approaches: A literature review with PROMISE repository dataset

Software testing is an important and critical phase of software development life cycle to find software faults or defects and then correct those faults. However, testing process is a time-consuming activity that requires good planning and a lot of resources. Therefore, technique and methodology for predicting the testing effort is important process prior the testing process to significantly increase efficiency of time, effort and cost usage. Correspond to software metric usage for measuring software quality, software metric can be used to identify the faulty modules in software. Furthermore, implementing machine learning technique will allow computer to “learn” and able to predict the fault prone modules. Research in this field has become a hot issue for more than ten years ago. However, considering the high importance of software quality with support of machine learning methods development, this research area is still being highlighted until this year. In this paper, a survey of various software metric used for predicting software fault by using machine learning algorithm is examined. According to our review, this is the first study of software fault prediction that focuses to PROMISE repository dataset usage. Some conducted experiments from PROMISE repository dataset are compared to contribute a consensus on what constitute effective software metrics and machine learning method in software fault prediction.

A Proposed surveillance model in an Intelligent Transportation System (ITS)

Progress in the complexity of large cities, highly complex systems, and intelligence science, in particular, smart city technology, has shown great ability in helping to reduce traffic congestion in developing cities. All ideas from the development of intelligent transportation to a town that wants to build and want to change into a smart city, especially in the field of ACP (system created, computing developed), based on parallel management and control system (PTMS). PTMS is considered to be enlarged to a new generation of an intelligent transportation system, and its essential component of architecture then make the hardware and software that will support a new architecture in a developing city to a smart city. The case in a lift is a communication system on a car that uses peer to peer networks and smart cards, with a communication system in a vehicle is expected to control congestion in a developing city through an original town with a connected system. This paper proposed four aspects of surveillance such as Traffic surveillance, vehicle Surveillance, passenger surveillance and driver surveillance. The combination of these surveillances will create best applied Intelligent Transportation Systems (ITS) or can be called as Smart Transportation Systems(STS).

Gamification in the e-Learning Process for children with Attention Deficit Hyperactivity Disorder (ADHD)

The technology that has developed at this time has been very advanced and very sophisticated, one industry that is growing with the times is the games industry, increasingly sophisticated games that make users of the game more spoiled with many things, such as graphics and technology, one of which is a part of the games industry is gamification. With gamification, a lot of people help people learn something natural, and gamification is usually inspired by everyday life, for example, the game The Sims, which combines daily life with a game in sports is one of the best examples of gamification implementation. Using gamification in a game makes learning more enjoyable than learning with truth, and without realizing it they have done learning. In this paper we proposed a gamification architecture for children with Attention Deficit Hyperactivity Disorder (ADHD) which include logic game such as brain, think, body, move, sport and logic. Moreover, there are five methods proposed for children with Attention Deficit Hyperactivity Disorder (ADHD) such as object game, content, and player, design, and framework, deploy and play, and finish structure.

A proposed framework in an intelligent recommender system for the college student

Abstract This article aims to proposed framework an Intelligent Recommender System (IRS) for students in higher education institutions. This conceptual framework includes problems in predicting student performance, the possibility of graduating on time, and recommends choosing subjects according to performance, and career interests, which are useful for assisting pedagogical interventions in future student development. The success in the development and implementation of the proposed IRS framework is inseparable from using data mining and machine learning techniques in predicting and providing recommendations. Data analysis consisted of clustering techniques, association rules, and classification using Support Vector Machine (SVM), Naïve Bayes, and k-Nearest Neighbour (k-NN). These techniques are used to solve problems related to students and to provide appropriate recommendations. The result is an IRS conceptual framework for the college student that can be used as smart agents to provide student guidance and suggestions to support the process of education in higher education.

Development Conceptual Model and Validation Instrument for E-Learning Succes Model at Universities in Indonesia: Perspectives influence of Instructor's Activities and Motivation

The purpose of this study is to develop conceptual models and validation instruments for the success of e-learning systems in Indonesian universities from factors that influence instructor activity and user motivation. Model development is based on the IS success model of DeLone and Mclean by adding instructor activity and motivational variables based on related literature studies. Evaluation is done by testing the reliability and validity of the instrument. Data collection by distributing instruments online with the snowball method, respondents obtained were 234 used as data analysis with SmartPLS 2.0 software. The results of this study produced a conceptual model consisting of 9 variables and 83 instruments which were validated according to the provisions. From these results, it can be implemented in future research by increasing larger respondents to get results that represent the success of e-learning systems at universities in Indonesia.

Personalized E-learning Model: A systematic literature review

The development of electronic learning models, especially in developing countries such as Indonesia has grown well as information technology applications designed for learning purposes. Community colleges and schools seek to complement the traditional teaching system with e-learning systems. However, we found there are differences in individual learning styles in terms of speed and learning styles. Serving or teaching students with one mechanism of the same teaching method will ignore individual rights while reducing the meaning of education broadly from the humanity dimension. Such situations will affect the target of increasing competence, the growth of knowledge and the value expected for some individuals to fail. The electronic learning model then undergoes a shift away from a mere system, now evolving into a personalized learning model, where learning processes are oriented toward the students' abilities. Under these conditions, models and other techniques are needed to help personalized adaptive learning as they need it. The purpose of this study was to identify the general criteria of personalized electronic learning model to meet the needs, interests and objectives of the learner in a more personal sense in a broader sense. This research was conducted through literature study on papers published in the last five years (2012–2017). The results show the common components, techniques or tools that are commonly used, as well as the support of the theoretical basis used as the platform for the development of a personalized e-learning model.

Combining intelligent tutoring systems and gamification: a systematic literature review

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

One of the challenging decisions for students is taking a job specialization. To make their decisions, they use subjective perceptions of friends or family due to the lack of guidance and limited resources. This increases the risk of dissatisfaction with the work environments. To address these drawbacks, this study presents a personalized career-path recommendation model (CPRM) to provide guidance and help college students choose information technology jobs. The design of the CPRM is based on the personalized Naïve Bayes (p-NB) algorithm with three primary sources: job profiles, personality types, and subjects. The association between personality type and college students was established using samples of 104 computer science students enrolled in private universities in Indonesia. CPRM was implemented as a web-based application. This study evaluated the model by measuring the quality of the recommended items to determine whether the proposed model is well accepted by users. The model considers educational data mining grounded theory (EDM-GT) data integration and hierarchically related concepts. CPRM has been validated by Information Technology (IT) professionals and three psychologists in Indonesia through focus group discussions. The evaluation results showed that more than 83% of respondents were satisfied with the recommendation model. Hence, CPRM can provide automatic academic advisors and guidance to computer science students interested in pursuing careers in IT jobs. The result shows that CPRM is the first career path recommendation model based on EDM-GT to target the computer science community in Indonesia.

A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology.

Lstm And Simple Rnn Comparison In The Problem Of Sequence To Sequence On Conversation Data Using Bahasa Indonesia

This study aims to implement and compare the Long Short Term Memory (LSTM) and Simple Recurrent Neural Networks (RNN) algorithm in the case of chatbot using Bahasa Indonesia data. The chatbot model used is a cahatbot model across business/service fields. The training data used in this research are the data on customer service talks with its customers in several business fields or services. To compare the models generated from the LSTM algorithm and Simple RNN algorithm, two tests were carried out, the first test is testing the chat output manually which was done directly by humans and the second test are comparing the LSTM algorithm and Simple RNN algorithms using the same training data and test data. From the experimental results, it was found that the chat output generated by the LSTM algorithm relatively can answer most of the tests correctly rather than Simple RNN algorithm. From the experiment, it was also found that the learning process in the LSTM algorithm takes longer than the learning process on the Simple RNN algorithm

The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm

This article aims to implement the algorithm model of k-Nearest Neighbor (k-NN) in analyzing, predicting, and classifying students who have potentials to get scholarships in universities. The k-NN algorithm works by making a prediction based on the closest data points between the old data history as training data and the new data as testing data. The data collected totals 1018 students with 24 scholarship receiver candidate students are used as the dataset for the test purposes. The attributes used in the prediction process are a semester, parents' income, number of family dependents, and Cumulative Grade Point Average. The distance calculation of the value from testing attribute to each training attribute uses Euclidean Distance equation, while the test of the model accuracy value is calculated using Confusion Matrix. The results of the simulation of the prediction model show that the determining factor of training data from both the number and the variation of different values can improve the performance of the k-NN algorithm with the best accuracy rate of 95.83 percent in predicting students who have the greatest chance of getting the scholarship.

The effect of UI, UX and GX on video games

We are presenting our research about the importance of the User Interface (UI) and the User Experience (UX) and Gameplay experience (GX) on a video game. In order to find the effect of user in forms of UI, UX and GX on video games, a quick survey was done using Google Form for 60 participants and only 52 respondents which answer yes in question number 1, where 36 and 16 respondents are male and female respectively. The 52 respondents are spread into 4 range ages where 16, 24, 8 and 4 respondents are between ages 13–20, ages 21–30, ages 31–40 and age more than 41 respectively. The interesting thing is from 52 respondents 69.2%(36 respondents) are male and 46.2% (24 respondents) between age 21–30, 34.6% (18 respondents) have been playing Tekken games, 71.2% (37 respondents) have ever played tekken 7 console version. For 37 respondents (71.2% from 52 respondents) who ever played tekken 7 console version, 67.6% (25 respondents) very love with score 5 for the Tekken 7 main menu, 64.9% (24 respondents) are very love with score 5 for the Tekken 7 character game, 56.8% (21 respondents) are very love with score 5 for the Tekken 7 character customization, 62.2% (23 respondents) are very love with score 5 for the Tekken 7 gameplay UI. The survey shows that most of the player are male, range age between ages 21–30 and Tekken game version 7 is loved because having excellent UI, UX and GX.

Django Web Framework Software Metrics Measurement Using Radon and Pylint

Nowadays, sites are complex applications that carry out transactions, render real-time information and offer interaction. Creation of web applications that allow many designers, advanced tools, and many more options. Web frameworks deliver a brilliant way between creating an application from the ground and using a content management system. This article centers around an open source web development framework, to be specific to Django. The methodology that used in the study is measuring Django Web Frameworks code quality metrics using Radon and Pylint. Django option in the main directory has 2,200 lines of code, Cyclomatic Complexity score is 16.375 considered as average complexity, and 6.69/10 by the pylint score.

Factors Affecting the Usage of Mobile Commerce using Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT)

Along with the development of the Internet, m-commerce become an alternative to shop online. This study aims to determine the factors that affect the user in using m-commerce to buy online using UTAUT (Unified Theory of Acceptance and Use of Technology) and TAM (Technology Acceptance Model). Constructs from the previous study are used such as performance expectancy, effort expectancy, social influence, and facilitating condition but also added perceived trust and perceived cost. Data obtained from 156 respondents were analyzed using PLS (Partial Least Squares). The analysis showed that performance expectancy, effort expectancy, social influence, and perceived trust significantly influences the use of m-commerce while facilitating condition and perceived cost have no significant effect on the use of m-commerce.

Smart Integrated Payment System for Public Transportation in Jakarta

Jakarta has many types of public transportation such as Kereta Rel Listrik (KRL), Transjakarta, taxi, angkot, metromini, mikrolet, bajaj, ojek, etc. But, there are some problems that happen in public transportation where the people don’t want to use public transportation and choose to use private transportation that cause more traffic in Jakarta. The problems of public transportation are in the facility which is still inappropriate, unclear information, uncomfortable, bad accessibility of public transportation which take more time to reach the destination, more complicated than taking the private transportation, and about the society, people don’t want to be labeled as low-class level. This paper is representing how to make the better system for public transportation in Jakarta with smart integrated payment system. Aim of this system is to encourage people to use public transportation rather than private transportation. The payment system that proposed are using smartphone application by scanning the QR Code or using smart card. User can easily pay through application by scanning QR code to pay the public transportation. The proposed of this smart integrated payment system will have opportunity as profit when collaborate with business when they can advertise their business and get customer from our user.

Perfecting A Video Game with Game Metrics

A state of a perfect video game is what developers has been seeking for their product developments, to achieve the selected state, several standards and methods needs to be applied. These standards and methods are special: they are both verifiable and quantifiable, to make their action and end goal a clear one, the terms for this standards and methods are called ‘game metrics’ and we decided that this is a must-have tool or method to be implanted in a development of a game because it will boost your standards rapidly and will be able to tell you about your own progress of the development.

Usability testing method in augmented reality application

Evaluation on system or software is important thing to do, when developing a software or system. This step will ensures yours developed system or software has high quality in functionality or non-functionality needs. Several methods can be used to evaluate the Augmented Reality application: subjective measurement using human perception or objective measure from observation, or evaluation by expert through cognitive walkthrough, heuristic evaluation, lab observation, and questionnaire. An evaluation is more valid if evaluation was done in multiple methods in order to confirm the result of evaluation.

The 3 steps of best data warehouse model design with leaning implementation for sales transaction in franchise restaurant

Doing Data Warehouse (DW) to your business or system is not only think about the trend only, but how to understand the DW knowledge itself and how to implement it. DW as a real technology of Artificial Intelligent (AI) which show up as how to think like a human, inevitably help the human particularly in making quick decision reports. Doing DW to your systems should apply to the mother knowledge of AI which we recognized as Software Engineering (SE) where we should apply best software application in more importantly we should satisfy the user. In order to make a good DW model design as user expectation then we should apply 3 steps such as collect all the needed reporting, order all the reports start from the most needed reports and mapping all the ordered reports into fact constellation schema. In this paper the 3 steps to model DW schema is applied in sale transaction in franchise restaurant as an example data. Building franchise restaurant must supported by information technology such as Data Warehouse (DW) in order to manage and control the business process, the branch, the sale, the staff and so on.

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

<p><span lang="EN-US">Frequent patterns in Attribute Oriented Induction High level Emerging Pattern (AOI-HEP), are recognized when have maximum subsumption target (superset) into contrasting (subset) datasets (contrasting </span><span lang="EN-US">⊂</span><span lang="EN-US"> target) and having large High Emerging Pattern (HEP) growth rate and support in target dataset. HEP Frequent patterns had been successful mined with AOI-HEP upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS with the number of instances of 48842, 569, 2458285 and 256932 respectively and each dataset has concept hierarchies built from its five chosen attributes. There are 2 and 1 finding frequent patterns from adult and breast cancer datasets, while there is no frequent pattern from census and IPUMS datasets. The finding HEP frequent patterns from adult dataset are adult which have government workclass with an intermediate education (80.53%) and America as native country(33%). Meanwhile, the only 1 HEP frequent pattern from breast cancer dataset is breast cancer which have clump thickness type of AboutAverClump with cell size of VeryLargeSize(3.56%). Finding HEP frequent patterns with AOI-HEP are influenced by learning on high level concept in one of chosen attribute and extended experiment upon adult dataset where learn on marital-status attribute showed that there is no finding frequent pattern.</span></p>

Software size measurement of knowledge management portal with use case point

Knowledge Management portal is a system to support Knowledge Management process, in order to create, capture, develop, share, reuse and optimize the knowledge and particularly in Bina Nusantara University which has implemented Knowledge Management System (KMS) since 2002. However, this KMS need to be measured in order to know how better this KMS in term of the software size. The BINUS KMS will be measured in term of their software size in functionality perspective with use case point method. This metric of KMS will be used by management to know how better the software size, complexity level and effort to development in numbering. Measurement of software size with software metric such as Use Case Point upon use case diagram for BINUS knowledge Management Portal shows that the project has medium software size with score Use Case Point (UCP) = 108.56 and has estimate effort will be developed in 2,064 hours (or in 258 days or 51.6 weeks or 12.9 months) and has development cost for 516,000,000.00 rupiah (Indonesian currency). Use Case Point, estimate effort and project value will powerful to help management in order to make decision regarding the implementation of IT software project development in term of time, money and people.

Software size measurement of student information terminal with use case point

Student Information Terminals (S-IT) is an independent academic service information system for students, where this service makes it easy for students to obtain academic information in real time with information such as the transcript of academic achievement, finance, course, attendance, exam, lecturer, card examinees and announcements academic, and has the function to print directly the data independently on S-IT devices. To find out how well the S-IT is in terms of software size, then needed a measurement. The measurements used in this paper using the Use Case Point (UCP) method as one of the approved software metrics which measure the functionality our software size. The results of the measurement of software size S-IT shown that the project has a small size, the software has a value of UCP = 96.767 estimate effort, has the development time 1,452 hours or equivalent 9 months 1 week and have development costs in Indonesian Rupiah is 263,175,000 IDR. The aims of measurement software size S-IT with the use case point is to help make decisions about the implementation of software development application project in terms of the estimated time, costs, and people.

Mining frequent pattern with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP)

This paper is extended version from previous paper which proposed AOI-HEP as novel data mining technique. This paper will explain how AOI-HEP mining technique can be used to mine frequent pattern. AOI-HEP is influenced by Attribute Oriented Induction (AOI) and Emerging Pattern (EP) mining techniques by applying AOI characteristic rule algorithm and improvement EP growth rate. The experiment used adult dataset from UCI machine learning repository with 48842 instances, run in 3 seconds and the instances were discriminated between government and non government concepts based on learning on workclass attribute. AOI-HEP mining interest for frequent pattern will be influenced by learning on their chosen attribute. The experiments showed that adult dataset which learn on workclass attribute had AOI-HEP mining interest for frequent pattern and there are four frequent patterns which have strong discrimination rule. Meanwhile, extended experiments upon adult dataset which learn on marital-status attribute showed there is no AOI-HEP mining interest for frequent pattern.

Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns

<p><span lang="EN-US">Frequent patterns in Attribute Oriented Induction High level Emerging Pattern (AOI-HEP), are recognized when have maximum subsumption target (superset) into contrasting (subset) datasets (contrasting </span><span lang="EN-US">⊂</span><span lang="EN-US"> target) and having large High Emerging Pattern (HEP) growth rate and support in target dataset. HEP Frequent patterns had been successful mined with AOI-HEP upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS with the number of instances of 48842, 569, 2458285 and 256932 respectively and each dataset has concept hierarchies built from its five chosen attributes. There are 2 and 1 finding frequent patterns from adult and breast cancer datasets, while there is no frequent pattern from census and IPUMS datasets. The finding HEP frequent patterns from adult dataset are adult which have government workclass with an intermediate education (80.53%) and America as native country(33%). Meanwhile, the only 1 HEP frequent pattern from breast cancer dataset is breast cancer which have clump thickness type of AboutAverClump with cell size of VeryLargeSize(3.56%). Finding HEP frequent patterns with AOI-HEP are influenced by learning on high level concept in one of chosen attribute and extended experiment upon adult dataset where learn on marital-status attribute showed that there is no finding frequent pattern.</span></p>

Heart Disease Classification Model Using K-Nearest Neighbor Algorithm

Heart disease is a disease that needs to be watched out for and is of particular concern. Seeing to the WHO report, in 2018, as many as 17.9 million people died from heart disease, and especially in Indonesia, heart disease in 2020 became the highest cause of death. This study uses data mining techniques to pull out information from the data used. This research provides a scientific contribution, namely detecting heart disease as early as possible. In this case, the author uses the K-Nearest Neighbor Algorithm to classify the data based on the nearest neighbor data. The database is own in a reasonably high volume, so it should note that irrelevant attributes will be removed over or noise. If they are still used, data processing results will not be optimal, so data cleaning needs to be done carefully. The selection of the data used was 1243 records, and after being selected the data were taken in this study as many as 366 records, with parameters using 12 attributes, actual data from hospitals, data consisting of data from patients under surveillance for cardiac care, and data from patients who underwent surgery and Data from Medical Examination. Therefore, it is necessary to develop a decision support system that assists doctors in taking steps for early detection. Research conducted with the K-Nearest Neighbors algorithm accuracy up to 77% with a value of K = 7.

Object-oriented modelling with unified modelling language 2.0 for simple software application based on agile methodology

Unified modelling language (UML) 2.0 introduced in 2002 has been developing and influencing object-oriented software engineering and has become a standard and reference for information system analysis and design modelling. There are many concepts and theories to model the information system or software application with UML 2.0, which can make ambiguities and inconsistencies for a novice to learn to how to model the system with UML especially with UML 2.0. This article will discuss how to model the simple software application by using some of the diagrams of UML 2.0 and not by using the whole diagrams as suggested by agile methodology. Agile methodology is considered as convenient for novices because it can deliver the information technology environment to the end-user quickly and adaptively with minimal documentation. It also has the ability to deliver best performance software application according to the customer's needs. Agile methodology will make simple model with simple documentation, simple team and simple tools.

Attribute Oriented Induction of High-level Emerging Patterns

Attribute Oriented Induction (AOI) produces highlevel characteristic summary data but does not discover new emerging patterns. Emerging Pattern (EP) algorithms discover emerging patterns between datasets but mostly consider low-level data. This paper introduces an algorithm, AOI-HEP, derived from both AOI and High-level Emerging Patterns (HEP), where HEP discriminates the high level data from AOL The main objective is to discover characteristic HEP patterns using AOI. To filter out the large overlapping and subsuming attribute values in the output, a Cartesian product of attribute values, a similarity metric based on attribute values and attribute hierarchy level are applied. Experiments used four datasets from the UCI machine learning repository. Results show that various interesting HEP patterns can be generated by using the AOIHEP algorithm.

Improving Rice Productivity in Indonesia with Artificial Intelligence

Indonesia is one of the biggest agriculture countries in the world with one of its major commodities, rice. Despite its vast paddy field and tropical resources, Indonesia still has not achieved food security. This paper explores ideas about how to present artificial intelligence that may increase rice productivity in Indonesia. Current situations of rice cultivation are mentioned start from existing technologies and systems used in Indonesia. Some artificial intelligence concepts are also introduced. Artificial Intelligence use in rice farming is then analyzed which ideas may be applicable to increase efficiency further. Those that are already implemented in rice farming include diseases and pest detection, prediction and estimation, and automated intelligent systems. In the proposed idea section, ideas to increase production are explored. Using advanced technologies may not be suitable for Indonesia's rice farmers. One argument is to minimize the differences in rice yield in different areas or so what we called yield gap. Factors such as weather, water, and harvest date are those with most impacts on the yield gap. It is beneficial to have a model that can predict the optimal planting date of rice since that will maximize the factors before. Among those implemented, a planting calendar prediction on rice-based on rainfall is already developed. However, rainfall is not the only factor. El-Nino occurrences and temperature changes also have a role in deciding when best to plant rice. The author suggests adding those two new variables, making three in total to the earlier neural network model may improve the overall result.

Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) future research

Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) as a new data mining technique, combines two data mining techniques i.e. Attribute Oriented Induction (AOI) and Emerging Patterns (EP). The AOI-HEP application is implemented as a hybrid between AOI characteristic rule mining and HEP algorithms. AOI-HEP combines the powerful features of AOI and EP by using concept hierarchy in AOI to generalize into high level data and applying growth rates in EP and produces powerful discrimination for high level data. AOI-HEP can be implemented to discriminate datasets such as finding bad and good customers for banking loan systems or credit card applicants and etc. Meanwhile, AOI-HEP can be implemented to mine similar patterns such as similar customer loan patterns or similar customer credit card rating and etc. Since AOI-HEP is a new data mining technique, then future research can be explored such as inverse discovery learning, learning more than two datasets, learning other knowledge rules and etc. AOI-HEP future research will give research idea for data mining researchers community particularly for bachelor and master degree students. Indeed, AOI-HEP as new comer data mining technique will be completed in discovery process, having rich interesting patterns and become interested mining technique.

Blockchain Technology-Based Good Distribution Practice Model of Pharmacy Industry in Indonesia

Distribution is the main activity in integrated product supply chain management. In the pharmaceutical industry, the process of drug distribution is important because of the handling, storage, and distribution of medicinal products with good standards and quality. The problem that occurs in the pharmaceutical industry is the circulation of counterfeit drugs by related parties, for example, unofficial or unregistered distributors or data collection for unregistered medicines circulated by distributors. Permits misused from drug manufacturing processes until they are distributed or circulated do not comply with the Food and Drug Supervisory Agency standard provisions. These problems must be resolved quickly with technological support to facilitate the distribution process in recording data distribution, providing data security, and traceability of transactions between related parties. This study proposes a good drug distribution model by applying blockchain technology. The Model development uses a qualitative approach and a user center design. The result of this study is a validated drug distribution model with blockchain technology. The model has the characteristics of transparency, security, traceability, decentralization, automation, immutability, and reliability. This model can help the government ensure public health and safety by ensuring that the drugs received are of good quality, thereby increasing the community safety and health and trust in drugs in circulation.

Enhanced IPCGAN-Alexnet model for new face image generating on age target

Cross aging face recognition ability will decrease to recognize someone's face after a certain time. Adding synthetics face images at a certain age generated from face aging architecture is one way to increase the performance of cross aging face recognition. A synthetics face image can create use the Generative Adversarial Network-based architecture. The current Generative Adversarial Network-based in face aging still needs high computation to create a model. Based on that reason we proposed a new optimal variant of Identity Preserving Conditional Generative Adversarial Network (IPCGAN), to generate a synthetic face image at certain age groups. In the proposed architecture, change made at the structure in the generator module, age classification module, and change the objective function to increasing the accuracy performance when generates a realistic synthetic face image in certain age groups and also speed up the training time. Modification in the age classifier at the proposed network forces our architecture to generate better synthetics face in certain age groups. Evaluation using Facenet, and Age prediction shows our method accuracy has 4.2% better results in k-NN classification, 3.6% better accuracy results in SVM classification, 8.6% better accuracy result in age verification, and 4.5% fewer accuracy results in age prediction.

Trends and Characteristics of Career Recommendation Systems for Fresh Graduated Students

Career Recommendation System (CRS) is an artificial intelligence solution capable of suggesting appropriate jobs or careers based on user profiles and industry needs. This study presents a systematic literature review that focused on variant characteristics of CRS and has been implemented in the last ten years. The review found 17 studies were extracted from ACM, IEEExplore, Science Direct, Springer, Willey, and MDPI databases. The results of this review prove that a hybrid recommender system is the most frequently (47%) approach implemented in CRS studies. Text mining (29,5%) is most commonly applied as the artificial intelligence technique in CRS. At least 7 features are needed to build a CRS model, but the most widely used are job profiles and course profiles with 71,42% and 35,71% frequency respectively. The most widely applied evaluation metrics is precision (21%), followed by acceptability, accuracy, and user response each 14% in review.

Developing of Indonesian Intelligent e-Health model

Indonesia as the most populated country in ASEAN has inadequate health care treatment among ASEAN countries and building e-health for Indonesia is an urgent matter to strength Indonesian healthcare treatment. Moreover, there is a critical to strength national health information system which have challenges such as weak coordination among central, provincial and district health office and inadequate using of information system for decision making, where need to integrate all healthcare data such as health facility, health promotion, health financing, Human Resource for Health (HRH), community participation and health management. The development of Intelligent e-health will concern in healthcare and will build collaboration for data collection with public and private community health center and hospitals, Healthcare Social Security organizing body (Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan), the ministry of health of the Republic of Indonesia and World Health Organization (WHO). The development will implement current technology such as Data Warehouse, Big Data, Data mining, machine learning, knowledge management, Decision Support System and so on which dealing with structured and unstructured data. The development of Intelligent e-health will be built in smartphone application, using the Internet of Things (IoT) and including mining Indonesian healthcare information from social media.

Smart Insurance System Model Concept for Marine Cargo Business

In this digitalization era, insurance companies have to face many new challenges, namely a very competitive market that has resulted in decreased margins and revenues and increased expectations from customers for insurance company services. Insurance companies are required to be fast-paced in providing accurate services and pricing. In addition, insurance companies must carry out marketing strategies that make customers feel valued so that strong engagement will arise. The focus of this research is on the field of marine cargo insurance, which is an insurance product that guarantees the loss of transportation of goods by sea. The purpose of this research is to analyze the underwriting and marketing processes in marine cargo business loss insurance and identify insurance models that can be smart and comprehensive solutions. The research method is to conduct a literature review to obtain previous problems and solutions related to this research topic. Then the researchers conducted observations and interviews with experts at an international insurance company in Jakarta. From this method, the main problems in this research are the underwriting process, product information services, and system recommendations that are less effective and efficient. This study proposes a smart insurance model concept by combining technological features, namely expert systems, chatbot, and recommend systems. It is hoped that this research will contribute to increasing underwriting and marketing productivity, as well as customer satisfaction in getting fast and appropriate product services.

Code-Mixed Sentiment Analysis Using Machine Learning Approach – A Systematic Literature Review

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis.

Acquiring Automation and Control Data in The Manufacturing Industry: A Systematic Review

Industry 4.0 has driven the need for Information Technology (IT) & Operational Technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as Programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure.

Mining Unstructured Data in Social Media for Natural Disaster Management in Indonesia

This paper proposed a model system for unstructured mining data in social media for natural disaster management in Indonesia. The model system of natural disaster management will be tested using real data where the application will be run from the stage crawl social media, tokenization, filtering, stemming, similarity measure, and Name Entity Recognizer so as to ascertain whether the software is built is in conformity with the rules of data collection events natural disasters that can be reliable. The proposed model system of natural disaster management can help the Indonesian government to calculate the impact of floods, landslides, and tornados that it could decide to focus fixes in the correct fields. If the government has made improvements by the mapping of disaster impact it will automatically proclamation of floods, landslides, and tornados in social media and news websites will decrease, and the value graph will change impacts linkages so that the government can focus subsequent repair.

Intelligent E-commerce for Special Needs

Special need is a person who need assistance for their disabilities which include medical, physical, mental or psychological such as person with autism, down syndrome, dyslexia, Attention Deficit Hyperactivity Disorder (ADHD), multiple sclerosis, schizophrenia, cystic fibrosis, cerebral palsy, muscular dystrophy, blindness, deafness, epilepsy, chronic asthma and so on. In this proposed research idea about intelligent E-commerce for special needs, we limited to four types of difficulties such as hearing difficulty, vision difficulty, language difficulty, and learning difficulty. In this paper, we interest in those special needs persons who can use e-commerce and how to model a system that can help them to joy and easy to join with e-commerce. For some, this intelligent e-commerce is not only for shopping only but will become part of their healing for special needs in order to engage with daily human activities and increase their daily confidence in life. This paper also a preliminary investigation that can be used for Ph.D. or Doctoral research degree who is interested in building e-commerce framework for people with a special need or disability.

Exploring Immersive Virtual Reality in Higher Education: Research Gap and Future Direction—A Scoping Review

The rapid advancement of technology in the post-COVID-19 era has positioned immersive learning as a transformative approach to enhance educational experiences. Despite its vast potential, recent research developments reveal persistent challenges and gaps that impede widespread adoption. This study conducts a scoping review using the PRISMA methodology to systematically analyze current literature, identify research gaps, seek the challenge, and propose future research directions. From an initial pool of 414 papers, 75 were selected, comprising 60 research studies and 15 review papers. Notably, 55 studies focus on immersive virtual reality (IVR) purely for educational enhancement in traditional academic settings, while 20 explore the implementation of IVR in education with gaming activities. The analysis indicates a predominance of mixed-methods research within education, computer engineering, and computer science. Most studies are limited by short durations (typically 30 minutes) and small participant groups (under 50), raising concerns about the generalizability of findings. Key themes identified include learning context (21 papers), learning design strategies (10 papers), and immersion elements such as avatars and haptic feedback (six papers). While positive impacts like increased satisfaction, motivation, engagement, knowledge enhancement, and usability are reported, negative effects such as motion sickness (13 papers) and dizziness (11 papers) persist. Crucially, only 11 studies exhibit high statistical power, underscoring the need for more robust research designs. Challenges identified encompass participant limitations, homogeneity, user discomfort, hardware unfamiliarity, and cognitive load—all intricately linked to design strategies. The implications of this review highlight the necessity for future research to focus on long-term studies, optimize user experience, develop cost-effective content creation methods, and integrate gamification into learning design. Addressing these areas is essential for overcoming current barriers and fully realizing the potential of immersive learning in education.

Building Damage Assessment Using Deep Learning: Bibliometric Analysis

Natural catastrophes have become one of the hottest subjects as a result of the harm they have caused to human structures. In recent years, research pertaining to the discipline of building damage assessment (BDA), which seeks to study the effect of damage on structures, has grown substantially. This bibliometric study seeks to determine the state of the art in the subject of Building Damage Assessment Using Deep Learning by examining several papers. The method used (Donthu et al., 202) divides the steps of bibliometric analysis into four parts: identifying the field of research to be analyzed, identifying the methods used in bibliometric analysis, collecting research data with similar fields, and executing and analyzing the results of bibliometric analysis on the collected data. In the data collecting and analysis portion (Garza-Reyes, 2015), the identification of data search keywords, the execution of data searches, the enhancement of search results, statistical data processing, and data analysis are carried out. Several research questions pertaining to trends, influences, themes, authors, and other crucial components of doing research in the area of building damage assessment are answered using these techniques. The outcome is the mapping and resolution of all research problems, as well as the acquisition of bibliometric analysis-related data. The provided findings have been mapped into several types of visualization, including network % clustering graphs, overlay graphs, and density graphs, as well as other forms of visualization that will aid researchers in comprehending the study circumstances in the field. It is envisaged that this bibliometric study would make it simpler for scholars to do research in related topics.

Rice Commodity Supply Chain Issues in Indonesia: Exploring Blockchain Technology as an Alternative Solution

Agriculture has long been recognized as pivotal to economic development, offering avenues to alleviate poverty, foster prosperity, and sustainably meet the food demands of a burgeoning global population projected to reach 9.7 billion by 2050. However, persistent inefficiencies within agricultural supply chains, particularly in sectors like rice production, pose formidable challenges. Addressing these inefficiencies is imperative to avert a surge in hunger and malnutrition. Failure to do so could condemn 815 million people to hunger and leave 3 out of 10 individuals malnourished. Thus, investigating solutions to enhance the resilience of rice supply chains is paramount. This research endeavors to identify and analyze challenges inherent in tracing the rice supply chain, offering a conceptual model proposal as a potential remedy. Employing a methodology centered on scholarly discourse and extensive literature review, our study reveals various impediments facing rice commodity supply chains in Indonesia. Furthermore, this study leverages blockchain technology, specifically the Sawtooth Hyperledger platform, to streamline the tracing process for rice commodities and rationalize the supply chain by reducing intermediary actors. From the proposed model, there is great hope that it can provide implications for the rice supply chain to run more efficiently.

Software Size Measurement of Smart Digital Tourism Project based on Use Case Point

In recent years, the tourism sector has evolved into a key strategic business for boosting local economic output. This industry has been dealing with the COVID-19 pandemic impact since the end of 2019. The tourism sector was, however, expected to bounce back and carry on expanding as it had in the past. For the purpose of enhancing services for stakeholders, this sector must transition from the conventional approach to digitization. Regarding the ability to include stakeholders in offering smart services, a smart digital tourism should be prepared. The creation of this clever solution should follow a quantifiable software development approach, such as measurable software design. The purpose of this study is to provide a case point method for measuring software design in relation to project budget and schedule. The study's findings showed that the size of the project would be 112.27 Use Case Points, it would take 2,133.13 hours to complete, and it would cost 28,530,613.8 Indonesian Rupiah (IDR) to develop the software. The findings will aid in the management of software development at a later stage in terms of making decisions and planning additional quantifiable projects.

Searching Routing using A-Star (A*) Search Algorithm

The rapid development of smart cities requires the integration of advanced technologies to maintain efficiency in urban areas. One critical aspect is the implementation of effective route-finding methods to ensure smooth traffic flow and enhance the quality of life for residents. This paper explores the application of the A-Star (A*) Search Algorithm within the context of Indonesia’s planned new capital in Kalimantan. The study employs the A-Star algorithm, incorporating various heuristic distance measures such as Euclidean, Manhattan, and Chebyshev distances. The methodology involves creating graphs and trees from input data to optimize route planning for both public and private vehicles. The study aims to design a flexible and efficient routing system that can accommodate the diverse driving styles and conditions specific to Indonesia, particularly for the proposed new capital city, which aims to be a hub of smart city innovations. The findings reveal that the algorithm’s flexibility allows it to adapt effectively to real-time traffic data and various environmental conditions. In conclusion, integrating the A-Star algorithm into intelligent city infrastructure presents a promising solution for managing traffic and transportation in Indonesia’s new capital. By leveraging IoT and real-time data, the algorithm can enhance the efficiency and safety of urban mobility, contributing to the overall success of innovative city initiatives in Indonesia. This study’s novelty lies in its adaptation of the A-Star algorithm to Indonesia’s unique cultural and environmental conditions, providing a customized solution for the country’s new capital city.

Blockchain Applications in Smart Cities for Environmental Sustainability and Tourism Enhancement

The rapid advancement of smart city initiatives has necessitated the integration of emerging technologies such as Blockchain to enhance governance, service delivery, and sustainable development. Despite the growing adoption of Blockchain across various smart city domains such as transportation, healthcare, waste management, and digital identity systems, its application in environmental sustainability and tourism enhancement remains underexplored. This study aims to systematically investigate the role of Blockchain technology in supporting smart city components that promote environmental conservation and urban tourism. A systematic literature review was conducted using the PRISMA method involving 772 Scopus-indexed articles from five major databases including IEEE Xplore, ScienceDirect, Springer Link, Wiley Online Library, and Hindawi. After a rigorous screening process, 37 relevant articles were selected for detailed analysis. The findings reveal that while Blockchain has been extensively applied in smart services and infrastructure management, its integration in areas such as city park management and the protection of urban green zones for tourism purposes is limited. The study identifies a significant research gap concerning the use of Blockchain to optimize eco-tourism governance and regional financial planning. It concludes that Blockchain holds strong potential to enable decentralized, trans-parent, and efficient management of environmental and tourism assets within smart cities. The implications of this research pro-vide a foundational reference for policymakers, urban planners, and future researchers to expand the technological scope of smart tourism and urban ecosystem preservation. Future work is recommended to explore quantitative modeling and real-world pilot implementations in developing urban regions to validate the practical benefits identified in this review.

Software quality model for Internet of Things governance

Despite the growth of the implementation of the Internet of Things (IoT) in the last decade, the IoT still continues to rise. In 2020 there will be an estimated 50 billion devices connected to the IoT. A crucial factor is the quality of service through quality governance software of IoT. The various traditional approaches of measuring the quality of software needs to be improved and adapted to the characteristics of the IoT. This study aims to provide an overview of software quality model for IoT based on ISO/IEC 25010 and information quality attributes of COBIT 4.1. Through literature review approach, we found the mapping and relationship between IoT characteristics and quality characteristics of software based on the quality of information. These results will be used as a basis for formulating the governance framework of IoT.

Smart City Implementation Modelling in Indonesia with Integration Platform Approach

Doing development using Smart City approach has become a necessity. The complexity of the problems facing the government requires a smart solution. Implementation problems of smart city still found in Indonesia, until now still needed a new breakthrough to speed up the implementation process. This paper presents the Integration Platform approach to be an alternative solution for the Smart City implementation model. This open platform concept leverages existing technology resources and applications for shared use. The contribution of this paper is to provide a new alternative solution for policy holders and decisions in government in making smart strategic plans in order to improve the quality of society and public services.

Prediction of guilt and shame proneness based on disruption to psychological contract: A new light for corruption prevention

Amid controversy over plurality and contestation of the meanings of corruption, previous reviews and studies showed that proneness to moral emotions, i.e. shame and guilt, can predict one's corruption behavior. To give a theoretical basis for the efforts of preventing corruption that is thick with emotional nuance, this present study employs disruption to psychological contract, i.e. psychological contract breach (PCB), as a predictor of moral emotions proneness. The study involving 265 employees (169 males, 96 females; Mage = 32.32 years old; SDage = 7.28 years) of four big private banks in Jakarta, the capital city of Indonesia, shows that PCB—with noting that, in this study, its scale operational scoring represents, reversely, the contract fulfillment—can predict Guilt-negative behavior evaluation (Guilt-NBE), Guilt-repair (Guilt-REP), and Shame-negative self-evaluation (Shame-NSE); all in negative directions, proved via simple linear regression analyses. Further analysis showed a more dynamic relationship between PCB and Guilt-NBE that fits to a cubic regression model. This study contributes to the axiological aspect of business psychology, especially in the ethical psychology of banking industry.

Game Development with Scrum methodology

Many methodologies are being used in software development, not only software can follow the process, but now games could follow the cycle of processes. Starting from the traditional waterfall model to agile methodology, many game developers are trying to produce the methodologies that could solve their problem in game development. In this paper, the type of methodologies will be shown on current game development. Also, this paper would suggest agile methodologies especially scrum over others in game development, and the reason behind that will be explained. Besides that, there are proposed game development methodologies that could solve the problem of game development by involving three phases, including the preproduction phase, production phase, and post-production phase. In the production phase, the sub-steps will have four sub-steps such as design, development, testing and review and the sub-steps will cycle as long as a result are unsatisfied.

Conceptual Model of Knowledge Management and Social Media to Support Learning Process in Higher Education Institution

Nowadays social media has tremendously transformed organizational business process of institution. Higher Education as a place with the majority generation Y, that advances to use technology should realize these situations. With this social media platform, institution may facilitate the knowledge transfer process, then to support collaborative learning from e-learning to social learning. Referring to this phenomenon, this research will design the integration of concepts of knowledge management and social media as a framework to identify the significant components and its relationship to support each other. In this research, we use a systematic literature review from journal and text book to construct this collaboration model. The outcome of this study is collaboration model of knowledge management and social media to support learning process in higher education institution.

Quality measurement for serious games

One kind of games based on its playing media is video game, which defined as a game that use any kind of computers as media. In terms of purpose, game has two types, which is serious game and non-serious game. As learning media, serious games need to have ability to motivate its player in order to play the game until the end so the player can finish the game while understanding learning materials given in the game. Therefore, the quality of serious game should be measured, like any other software. When it comes to measure serious games, previous models can't do the job well since all of them have no way to measure the game's content, especially to measure the content that can improve player's motivation. This paper proposes a new game measurement metric to measure the game quality factors, especially to measure serious game contents that can motivate its players. The result of this paper is a metric table consisted of measureable factors and type of test to measure them.

Penentuan Rute Pengiriman Barang Dengan Metode Nearest Neighbor

Semakin cepat barang sampai ke konsumen maka menjadi lebih mudah untuk mendapatkan barang dan keuntungan perusahaan semakin bertambah. Pada pendistribusian membentuk salah satu pemecahan masalah untuk mencari rute dengan meminimumkan jarak dari lokasi gudang ke toko dan memiliki jumlah permintaan barang yang berbeda-beda. Menggunakan metode nearest neighbor untuk menyelesaikan penentuan rute distribusi barang dari gudang ke toko, dengan tujuan mengurangi total jarak pengiriman, waktu dan beban biaya yang dibebani perusahaan. Hasil pencarian rute menggunakan metode nearest neighbor menghasilkan jumlah rute paling sedikit dibandingkan dengan sebelum menggunakan metode dan pada total jarak dengan menggunakan metode 98610 meter atau 98,61 km sedangkan jika pada rute sebelum mengunakan metode 124198 meter atau 124,198 km terjadi pengurangan jarak 25588 atau 25,588 atau sebesar 20.6026 %.

Quality measurement of android messaging application based on user experience in Microblog

There are many options of android messaging application which give opportunity to user in order to choose which one as best or famous android messaging application and make it become suitable for them. Usually, people used to look at the information about best or famous android messaging application by texting in search engine such as google and get some link information from user/blogger reviews, and based on that reviews they will make decisions which one as suitable for them. We proposed the other way how to measure the quality of each android messaging application based on user experience which they text in Microblog such as Twitter. The unstructured data in the Microblog will be processed with 2 operators for sentiment analysis method in RapidMiner such as AYLIEN and ROSETTE. AYLIEN sentiment analysis has 3 categories such as positive, negative, and neutral, whilst ROSETTE sentiment analysis has 2 categories such as positive and negative sentiments. Finally, the finding sentiment analysis with these 2 operators will be compared with PlayStore review.

Detecting documents plagiarism using winnowing algorithm and k-gram method

In this paper, we propose and evaluate a web-based software to check similarities of documents. The resemblance value of those documents will be compared based on the percentage of its word resemblance. The similarity value will help to detect plagiarism in documents. Methods used in this application are winnowing algorithm and web-based k-gram. We evaluate the accuracy of the system by comparing the system result with the human result. The differences between the systems and the respondents are 7% with k-gram 25 and 4% with k-gram 20. Moreover, processing time of our application are also discussed.

Matchmaking Problems in MOBA Games

<p><em><span lang="EN-US">MOBA is a popular genre that requires teamwork to achieve victory. A close and tight match is what make MOBA fun to play and increase its user satisfaction, but some factor may ruin the matchmaking and create unbalanced match between the two team. Those problems factors are high latency, players with bad attitude, and players doing unfamiliar role. We use DOTA 2 as our case study. Then we compare the DOTA 2 matchmaking system in other sector to make comparison. Lastly, we discuss about solution to solve MOBA matchmaking problem such as displaying live information about online players, players searching for games, servers online and ETA for gaming to start. In addition, we proposed new variable to be considered in the matchmaking system, which are Preferences Role, player’s chosen preferences role will be considered while the system set up the game to minimize the number of unbalanced games in MOBA.</span></em></p>

Desain Model Data Warehouse dengan Contoh Kasus Perguruan Tinggi

The growth high education has been raising the competition in high education market and Data Warehouse can be used as an effective technology weapon for going to battle in high education competition market. Data Warehouse can produce the reliable reports for high level management on high education in short time in order to make the faster and best decision making for not just only increasing number of students, but possibility to find mining fund which have never thinking before limited by high education ordinance. The reports which produce by Data Warehouse are made based on database which summarization transaction database can be trusted and far from manipulation, different with old producing reports based on transaction database which need to be questioned and need time to produce, need manipulation for the result, cannot be trusted and could be have different results if differentiate to other systems. Data Warehouse will be modeled with dimension business concept and be created based on hypercube which produced based on high demand reports which usually used by high level management. In every fact and dimension table will be inserted with fields which represent the construction merge loading as an ETL (Extraction, Transformation and Loading) extraction. Keywords: Data Warehouse, High Education, Hypercube, Business Dimensional Concept

Early investigation of proposed hoax detection for decreasing hoax in social media

Social Media allow people to communicate over long distance and increase the users for the past years. However the threat of Hoax is also increasing in social media which increase the risk of mass panic, where the society become confuse between false and true, other words between hoax and not hoax. To mitigate the effect of hoax, a system to filter out hoax posts from social media is proposed. We propose a system framework that might be able to filter out hoax from social media post feeds in order to reduce the amount of hoax posts in social media. It is difficult to reduce 100% hoax from social media, but at least there is a technology which can decrease the hoax in social media. This system works by utilizing data mining to search past hoax records and analyze the post data pattern to determine whether a post is a hoax or not hoax. Our proposed system will also utilize social media Application Program Interface (API) such as news, facebook, twitter, in order to find similar writing in other social media or news website to find out the authenticity of a person's post.

Student performance prediction using simple additive weighting method

In the world of student education is an important component where the role of students is as someone who is psychologically ready to receive lessons or other input from the school. However, each student has different performance and development, therefore it is important to do monitoring so that student performance will always be monitored by the school for improving student quality maintenance. Also, in the process of valuing education for students needs to be done by giving an appreciation in the form of giving gifts or just giving words and motivation so that students can perform better in learning and participating in other activities at school. In terms of selecting students with good performance or those who have a very declining development using the school method not only assess students by one criterion but with several criteria to produce a decision that can be accepted by many people. Performance Students must also be monitored by the school or the related rights. In this paper, the student performance prediction was assessed with 5 criteria components and the result shows there are 10 very satisfy students, 10 satisfying students, 10 well students, and 10 Enough students from sample 40 students.

Recent Generative Adversarial Approach in Face Aging and Dataset Review

Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks. In this study, we review a classic approach that uses a generative adversarial network. The structure, formulation, learning algorithm, challenges, advantages, and disadvantages of the algorithms contained in each proposed algorithm are discussed systematically. Generative Adversarial Networks are an approach that obtains the status of the art in the field of face aging by adding an aging module, paying special attention to the face part, and using an identity-preserving module to preserve identity. In this paper, we also discuss the database used for facial aging, along with its characteristics. The dataset used in the face aging process must have the following criteria: (1) a sufficiently large age group in the dataset, each age group must have a small range, (2) a balanced distribution of each age group, and (3) has enough number of face images.

Measure The Level Of Success In Using Google Drive with the Kuder Richardson (KR) Reliability Method

The Google Sheets is a cloud technology part of Google Drive. Google Drive is a cloud operating system owned by Google's search engine. National Elementary School (SDN) to make a report book or Basic Education Databook (Dapodik) still use Microsoft Excel, which has the effect of delaying the delivery of report results to parents. This happens because to fill in the DAPODIK score must be filled by teachers who teach subjects in class. This study aims to provide knowledge to teachers in collaborating to fill DAPODIK and measure how reliable the delivery of material received by each teacher. Using the method of reliability Kuder Richardson KR-20 and KR-21 produces a value before the training on reliability values KR-20 = 0.6328, and KR-21 = 0.6000, after the training, the reliability values KR-20 = 0.5788 and KR-21 = 0.6231. From these results, it is known that the value after training for KR-21 has increased and according to the conventional correlation coefficient. In future research can be re-developed by making application system recommendations in determining teachers in schools to become instructors in accordance with the capabilities possessed based on the reliability testing results of the KR-20 and KR-21.

Innovation Architecture Smart Car Parking System with Wireless Sensor Networks

Nowadays, the number of cars is increasing every year, with the number of cars increasing, the parking areas will be limited. If parking areas can take more space in a town/place, it will be good for the town itself because nowadays the problem is either no parking space or too much parking space which is not suitable for human space. Moreover, because our population keeps going up and space is reducing even, we have to cut the forest to make space for parking lots, which is not suitable for our ecosystem. The number of cars itself also increasing each year, which is so problematic in road and infrastructure management. To fix this, we need to have a sound management system for maximizing the use of available space in town and minimizing the traffic in the road. The problem nowadays is a roaming car that searching for parking space and making road traffic and making massive pollution for the town. When a car searching for a parking slot inside a building that is crowded, it will go around the parking lot, and there will be traffic inside the parking lot because many cars will queue searching for empty parking slots. This means that the driver will also waste many fuels, which is not suitable for the ecosystem. Since this problem has not been fixed yet and the population is increasing each year, and a new way of driving and parking system is required, we want to propose a new system that uses the processor board, wireless sensor network to fix the problem.

Intelligent Tutoring System: Learning Math for 6th-Grade Primary School Students

This paper proposes a web-based application designed to help elementary school students who have difficulty learning online independently and also their parents who are currently having difficulty teaching their children to study at home online, especially at this time of difficulty with a pandemic outbreak like COVID-19; this time does not allow for physical meetings for the learning process in primary schools. In this paper, we only focus on mathematics because based on several other studies, it is very difficult and important to learn mathematics at the beginning of educational activities such as at the elementary school level. In this paper, the system is modeled using the Unified Modeling Language (UML) tool in the form of a use case diagram which is used to describe the proposed business process and uses class diagrams to describe the database model diagram. In this case, the class diagram is used to describe the data in the class diagram where each class refers to a table in the database. The web-based application user interface is shown at the end to show the communication between users and applications, where this web-based application is implemented using Personal Home Pages (PHP) as server programming and using MySQL to store database model designs. Moreover, for the Intelligent Tutoring System (ITS), content was created using the Cognitive Tutor Authoring Tools (CTAT) which is an authoring tool for learning mathematics created by Carnegie Mellon University. In the end, this web-based application is expected to be used and support teachers as a complement to online mathematics learning, especially during difficult times such as during the COVID-19 pandemic.

Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..

Implementasi Big Data untuk Pencarian Pattern Data Gudang Pada PT. Bank Mandiri (Persero) Tbk

Big Data bukanlah sebuah teknologi, teknik, maupun inisiatif yang berdiri sendiri. Big Data adalah suatu trend yang mencakup area yang luas dalam dunia bisnis dan teknologi. Big Data menunjuk pada teknologi dan inisiatif yang melibatkan data yang begitu beragam, cepat berubah, atau berukuran super besar sehingga terlalu sulit bagi teknologi, keahlian, maupun infrastruktur konvensional untuk dapat menanganinya secara efektif. Bank Mandiri adalah perusahaan yang bergerak di bidang perbankan, perusahaan ini salah satunya adalah melayani pemesanan barang antar Wilayah, Area dan Cabang dari seluruh Indonesia. Dalam proses pelaporannya, staf gudang masih menggunakan data yang di sediakan dari system yang sudah ada, namun data yang di sediakan masih dalam bentuk laporan data biasa yang di hasilkan dari OLTP dan data yang bersifat tidak dapat di ubah, sehingga laporan yang di berikan kepada management tingkat atas sebagai bahan analisa dalam pengabilan keputusan kurang informatif. Penulis akan mengembangkan aplikasi yang dapat mengolah dan melakukan pencarian pola data sebagai bahan pelaporan, implementasi teknologi big data akan sangat membantu proses pengelolaan data pada aplikasi tersebut, dikarenakan data yang di kelola dalam kurun waktu yang cepat akan terus bertambah, sehingga pengelolaan data menggunakan teknologi big data menjadi solusi untuk dapat mengolah data dalam melakukan pencarian pattern pada data gudang Bank Mandiri. Aplikasi yang akan di kembangkan tersebut akan menyajikan informasi-informasi yang di butuhkan seperti pattern barang yang paling banyak di pesan dan pattern user yang paling banyak melakukan pemesanan, sehingga pattern pada aplikasi tersebut akan membantu staff dalam melakukan pelaporan dan Manegement tingkat atas dalam melakukan analisa dalam pengambilan keputusan.

Measurement of QuestDone mobile application using 7 steps use case points method

The rise of mobile application is inevitable. Every year, the number of mobile application is increased. It is important for mobile application project owners to calculate the required resources before building a mobile application. In software metric, Use Case Points method is able to count software size of mobile application based on their functionality. This method utilizes use case diagram as their computation factors in the estimation process. Moreover, two other complexity factors are also considered in this method, which are: Technical Complexity Factor and Environment Factor. In this paper, we present software size calculation of QuestDone Mobile Application using 7 steps use case points method. QuestDone has been implemented, but we do not know its software size (i.e. how big the software, how much it cost, how many people is needed). As the result from use case points method, the Use Case Points value of QuestDone is 126.88 with Effort Estimation equal to 889 hours. The software size estimation process of QuestDone Mobile Application detailed in this paper can give an insight to project owners to count software size of other similar projects.

A prototype of Baby Monitoring Use Raspberry Pi

The security system in monitoring the room or something suspicious activity that can cause crimes in the field of information and technology as in private homes is indispensable. Aims to keep valuables from theft and loved ones like babies from kidnapping or reducing the risk of an accident in an unguarded infant activity. The use of CCTV still has constraints because some functions are still done manually such as should always move the record of the CCTV data every day to different memory so that there is waste in using memory. Moreover, the position of the camera can not be adjusted freely in the placement so that it can easily be destroyed by irresponsible people, and also ordinary CCTV needs electricity that is considerable in its use. This research is done to improve and facilitate the process of monitoring of baby activity and the room where the baby is called sleep or rest, all monitoring activities can be done using only a Web browser that can be accessed by a smartphone or desktop. The monitoring system is created using a program called RPi Cam that is installed on the Raspberry Pi with a Raspberry Camera module as a monitoring camera and a DHT22 sensor module as a room temperature monitor.

Card game element rising academy to improving decision making ability

Mobile Game is very popular for many people and can be played anytime, anyplace. “Elements Rising Academy” developed to make user who play this game increased in decision making and problem solving ability. more. Game developed use Waterfall framework and modeled use Unified Modelling Language (UML). This research result this game very entertaint, interesting, and easy to play and game increasing the problem solving and decision making skill.

Key strategic issues pharmaceutical industry of SCM: A systematic literature review

In this global era, making the competition in the pharmaceutical industry is very treacly. Implementation Supply chain management is the process of planning and managing all sourcing, procurement, distribution activities to increase value to customers and interested companies. Pharmaceutical companies are one that has a very complex supply chain. In this article will discuss the main issues that can improve industry strategic. This research is done by the systematic approach of literature to find things related to research. This study used 64 articles from search results. The results of this search are key issues in the pharmaceutical industry such as product and process development, capacity planning, factory and network design, e-business and IT applications, inventory management, outsourcing and reverse logistics, lean manufacturing, performance measures, people, and information technology. All of these findings are issues that improve the performance of supply chain management in the pharmaceutical industry.

Perfecting A Video Game with Game Metrics

Hoax Classification in Imbalanced Datasets Based on Indonesian News Title using RoBERTa

Hoaxes are something that can not be avoided, especially in Indonesia, where the literacy rate in Indonesia is quite low, they are easy to believe in news without doing fact check. The worst thing is that news that is trusted by the public may not be read completely through the entire content. They believe, from the title alone could already covers the entire content of the news. Media in the other side, are also competing to make controversial titles so that their traffic is improved. The research that will be carried out by us is where we can classify a news whether it is a fact or a hoax from the title alone. The RoBERTa (A Robustly Optimized BERT Pretraining Approach) model will be used in this study, because in several previous studies RoBERTa has proven to be good for classification. The accuracy achieved in this study also reached 99.52% with an accuracy validation of 93.84% which shows that even with an imbalanced dataset the classification shows a promising result by using the RoBERTa model which data is balanced using the undersampling method.

Prediction of self-efficacy in recognizing deepfakes based on personality traits 

<ns4:p> <ns4:bold>Background:</ns4:bold> While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual’s self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people’s personality traits influence their efficacy in recognizing deepfakes? <ns4:bold>Methods:</ns4:bold> Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults. <ns4:bold>Results:</ns4:bold> The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it. <ns4:bold>Conclusion:</ns4:bold> Self-efficacy in spotting deepfakes can be predicted by certain personality traits. </ns4:p>

Security Risks and Best Practices for Blockchain and Smart Contracts: A Systematic Literature Review

The rise of blockchain technology and smart contracts has brought widespread attention due to their capacity to transform multiple industrial sectors through decentralized, transparent, secure transactions. However, despite their promise to revolutionize various fields worldwide, lingering concerns regarding security risks impede their adoption rate. Addressing these concerns is crucial now more than ever; therefore, we conducted a comprehensive literature review within our study's scope that focused on published papers between 2014-2023 centered around security risks concerning blockchain and smart contracts. Our systematic approach using the PRISMA checklist analyzed nine categorized research model-based primary studies while recognizing vulnerabilities in smart contract development and providing best practices to mitigate such issues. These findings benefit both researchers and practitioners as they showcase how acknowledging these vulnerabilities can further develop into exploring more significant aspects of blockchain technology's security issues and smart contract development processes. Our study contributes significantly by expanding knowledge in this field while providing novel insights valuable for individuals involved in designing or implementing blockchain technologies.

Confidence of AOI-HEP Mining Pattern

Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) has been proven can mine frequent and similar patterns and the finding AOI-HEP patterns will be underlined with confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern, and each dataset as confidence AOI-HEP pattern between frequent and similar patterns. Confidence per AOI-HEP pattern will show how interested each of AOI-HEP pattern, whilst confidende per dataset will show how interested each dataset between frequent and similar patterns. The experiments for finding confidence of each AOI-HEP pattern showed that AOI-HEP pattern with growthrate under and above 1 will be recognized as uninterested and interested AOI-HEP mining pattern since having confidence AOI-HEP mining pattern under and above 50% respectively. Furthermore, the uniterested AOI-HEP mining pattern which usually found in AOI-HEP similar pattern, can be switched to interested AOI-HEP mining pattern by switching their support positive and negative value scores.

Supervised Classification Karakter Morfologi Tanaman Keladi Tikus (Typhonium Flagelliforme) Menggunakan Database Management System

Tanaman Keladi Tikus memiliki potensi medis tinggi dan bermanfaat dalam penyembuhan berbagai penyakit, seperti kanker payudara, kanker rahim dan leukemia. Tanaman keladi tikus memiliki keragaman genetik rendah karena pada umumnya tanaman ini diperbanyak melalui pemisahan bonggol secara vegetatif. Salah satu metode peningkatan keragaman genetik antara lain mutasi iradiasi sinar gamma. Uji coba peningkatan keragaman genetik ini menghasilkan data karakter morfologi dari tiap klon tanaman Keladi Tikus. Untuk menemukan pola dari data karakteristik morfologi tersebut, maka perlu dilakukan klasifikasi berdasarkan tingkat kesamaan dari data-data klon tersebut. Klasifikasi sebagai salah satu teknik data mining yang terukur, dapat dipercaya dan memenuhi suatu standar yang telah disepakati. CRISP-DM adalah standarisasi data mining yang digunakan pada penelitian ini. Untuk mengembangkan aplikasi klasifikasi data Mining tersebut digunakan bahasa pemrograman PHP dan Database Management System yaitu MySQ. . Berdasarkan penelitian dan setelah dilakukan pengujian, maka didapat perangkat lunak yang dibuat dapat digunakan untuk melakukan perhitungan tingkat similaritas dan melakukan klasifikasi pada dataset morfologi tanaman Kelati Tikus . Tanaman Keladi Tikus memiliki potensi medis tinggi dan bermanfaat dalam penyembuhan berbagai penyakit, seperti kanker payudara, kanker rahim dan leukemia. Tanaman keladi tikus memiliki keragaman genetik rendah karena pada umumnya tanaman ini diperbanyak melalui pemisahan bonggol secara vegetatif. Salah satu metode peningkatan keragaman genetik antara lain mutasi iradiasi sinar gamma. Uji coba peningkatan keragaman genetik ini menghasilkan data karakter morfologi dari tiap klon tanaman Keladi Tikus. Untuk menemukan pola dari data karakteristik morfologi tersebut, maka perlu dilakukan klasifikasi berdasarkan tingkat kesamaan dari data-data klon tersebut. Klasifikasi sebagai salah satu teknik data mining yang terukur, dapat dipercaya dan memenuhi suatu standar yang telah disepakati. CRISP-DM adalah standarisasi data mining yang digunakan pada penelitian ini. Untuk mengembangkan aplikasi klasifikasi data Mining tersebut digunakan bahasa pemrograman PHP dan Database Management System yaitu MySQ. . Berdasarkan penelitian dan setelah dilakukan pengujian, maka didapat perangkat lunak yang dibuat dapat digunakan untuk melakukan perhitungan tingkat similaritas dan melakukan klasifikasi pada dataset morfologi tanaman Kelati Tikus .

Literature Review of Religion Video Game

In this current digital era, the video game is favorite especially among young people. Besides widely known as a form of entertainment, some messages or lessons can be learned from a video game that may affect the players. Conscious or unconscious, games have influenced many sectors of life, and it has been recognized as a serious game and no exception to religion. Human as a living creature, like or dislike or even as an atheist should believe with something out there, and religion is as a medium which connects human with something out there. The game as a representation of entertaining, intelligent application will represent joy and happiness in human life's activities, and inevitably games will influence human religion. In the end, religion game will help the human to increase their faith in those their belief based on their religion. Similar to Information Technology which is implemented in business to increase revenue, the game as an entertaining intelligent application will increase human's faith. One kind of video game that provides it is religion video games which are the result of the incorporation between religion and video games. In this paper, we do a literature review about religion video games. We try to summarize how religious elements can be incorporated into a video game which can be seen from the parts, components, and approaches of the religion video game itself. We also try to summarize the positive side and controversies in religion video games.

Network Scanning Topology Based on Inventory Using Query SNMP Method

Understanding the Definitions of Microcredentials in Higher Education: Systematic Literature Review

The growing focus on microcredentials emphasizes the urgent need for precise and widely accepted definitions, as existing uncertainties hinder their effective implementation. This research aims to investigate the comprehension of microcredentials definitions in the context of higher education by conducting a systematic literature review. The goal is to identify current definitions of microcredentials to facilitate standardization initiatives. Following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to systematically search in Scopus database within 2019-2024 resulting $\mathbf{9 4 0}$ articles, by removing the duplicates, $\mathbf{2 8 0}$ articles were initially reviewed, with 29 selected for detailed thematic analysis based on specific inclusion and exclusion criteria. By adapting the six phases of thematic analysis, selected articles were identified analyzed, organized, described, and themes found the data set were reported. Key findings reveal that, while a universally accepted definition is lacking, common themes emerge: microcredentials are competency-based, modular, portable, stackable, and often include quality assurance. These attributes highlight microcredentials as valuable for upskilling and reskilling in a flexible manner. The study concludes that establishing standard definitions can increase the recognition and utility of microcredentials across educational and professional sectors and recommends further research to strengthen core elements for consistency.

Implementation of Mobile game for Religion Learning

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

Extended E-Learning Model to Support Home Schooling with Collaboration Between Teacher, Parents and Student

Homeschooling is a family and environmental/informal education path. Homeschooling is the practice of instructing one's children in one's own home rather than sending them to a school or other institution that follows a more conventional model of teaching and learning in a group setting. Problems for children who follow homeschooling are related to child psychology and boring learning methods. collaborative learning is a learning concept by involving several parties to increase the effectiveness and efficiency of learning. By involving many parties who have different competencies, they can solve homeschooling children's problems. On the other hand, e-learning with the Learning Management System can manage the learning process, teaching materials and collaborative processes in homeschooling in a more systematic and structured manner. With some of the phenomena above, this research aims to increase the effectiveness and efficiency of the homeschooling learning process by integrating the concept of collaborative learning and the Learning Management System. The research method uses a qualitative approach through observation to identify problems related to the learning process with homeschooling. Observations and literature reviews were also carried out to find alternative solutions with an information technology approach

Analysis of Enterprise Architecture Research Trends for Higher Education Institutions Using Systematic Literature Review and Vos Viewer

Enterprise Architecture Framework (EAF) is a commonly used framework and the best solution for addressing and defining the transformation that needs to be built to support business operations. To be able to make the transition quickly, many higher education institutions (HEIs) are starting to adopt EAF. This research aims to help the evolution and transpiration of enterprise architecture research in higher education. This research uses a systematic literature review (SLR) research method which includes several steps, namely identifying research questions, identifying research sources, using keywords to complete the data discovery process, disseminating data, and analyzing the results to answer research questions. The data used comes from digital libraries, especially Emerald Insight, IEEE Xplore Digital Library, and Science Direct. Data taken from 2019 to 2023 in RIS format will then be analyzed and visualized using VosViewer. The results of this research are to find the growth of research publications over five years, find research trends on enterprise architecture frameworks, identify relationships between scientific concepts, and determine the knowledge network of enterprise architecture frameworks based on keywords.

Usefulness of Honeypots Towards Data Security: A Systematic Literature Review

The increasing data volume given by the exponential growth of digital devices, cloud platforms, and the Internet of Things (IOT) had become an attractive target for attackers. This makes the search for innovative defense mechanisms intensified leading to a renewed focus on the utilization of honeypots to improve data security. This Systematic Literature Review (SLR) examines the usage and contributions of honeypots in data security field across diverse sectors and environments. Analyzing 38 selected articles from 2018 to 2023, the study highlights the growing adoption of honeypots as tools for detecting and preventing data breaches, exfiltration, and other security incidents. Our finding found that honeypots, with their deceptive capabilities, have proven could be deployed in sectors like healthcare, cloud computing, and industrial control systems and effective in detection and prevention data breaches, exfiltration, and other data breaches, making them as a crucial tool in the data security protection. The future of honeypots lies in their integration with machine learning and AI, to predict and counteract sophisticated. Moreover, evolving honeypots into tools for deterrence and corrective action opens new avenues for cybersecurity strategies. Their ability to act as deterrents by increasing perceived risks for attackers, coupled with their role in aiding recovery from data breaches through detailed forensic analysis, positions honeypots as a cornerstone in the development of resilient data security frameworks. This study paves the way for a more adaptive, proactive, and comprehensive approach to data security, highlighting the indispensable role of honeypots in the ever-evolving landscape of cybersecurity.

Mobile game Application for Religion engagement

Religion is a part of a fundamental human right to serve and worship their God according to their religion and belief. Mobile game application along with the development of mobile phone technology such as a smartphone, tablet, and laptop, can be used to increase the player or user interest to engage with religion thing. Using the mobile game application in mobile gadgets will expand quickly to install and use for religion engagement. The player can learn religion things with fun and entertain ways, where the player does not realize that they do something serious in an unserious way. This paper will explain the model system design for mobile game application for learning Catholicism in fun and entertaining way and for the first development was limited to only three Catholic catechisms such as bible learning, church lesson, and liturgy celebration. For the early development, three types of games were developed such as True or False game, Scramble words game, and multiple-choice game. The mobile game model was designed with a use case diagram, storyboard, and class diagram, where for the current implementation the mobile game application was implemented using a ten tables database.

Prediction of Road Infrastructure Priorities in Banten Province Using Analytical Hierarchy Process Method

Roads are the key infrastructure that is needed to support the smooth transportation to improve the condition of the community economy. Along with the passage of time and the use of the road, the road will suffer damage due to natural factors as well as other technical factors, so it takes serious handling efforts from the local government to maintain excellent road infrastructure for the community. In case of road damage handling in some areas still found problems that need to be resolved immediately, one of them is the problem of availability of budget that is not comparable to the level of damage of the road is quite a lot required a method to know the order of the road priority should be addressed immediately. To determine the prediction and priority order of road handling, the Analytic Hierarchy Process (AHP) algorithm is used with consideration of damage, traffic, and economic factors as criteria decision. The result shows that area of Serang city with the value 0.231 (23.1%) as the most priority road to be repaired.

Immersive Learning Model for University (ILMU): A Novel VR-Based Distance Learning in Higher Education

The rapid adoption of Immersive Virtual Reality (IVR) distance learning in higher education necessitates cohesive frameworks to guide its effective implementation. However, existing models remain fragmented, lacking integration in pedagogy, technology, and institutions. This study addresses this gap by proposing the Immersive Learning Model for University (ILMU), a model tailored for higher education institutions adopting IVR-based distance learning, developed through the Design Research Methodology (DRM). The research systematically identifies critical success components via a scoping review of 227 studies, revealing six clusters: Learning Design, Technology, Immersion, Engagement, Interactivity, and Usability. These components were refined through Delphi verification with stakeholders (academics, developers, and users), resulting in 15 validated components. The ILMU model integrates these components into a layered structure, emphasizing institutional alignment (Standard, Policy & Curriculum), adaptive pedagogy, and immersive technological synergy. A quasi-experiment involving 80 students compared IVR-based learning (using Nusameta apps) with traditional Zoom instruction. Results demonstrated significant learning gains for the IVR group (mean post-test: 77.75 vs 72.00; p = 0.042), validated by non-parametric tests (Mann–Whitney U = 592.50). The study highlights ILMU’s capacity to increase learning effectiveness, reduce cognitive load, enhance engagement, and align with institutional policies while addressing hardware limitations and ergonomic design challenges. By bridging theoretical rigor with empirical validation, ILMU offers a scalable framework for universities transitioning to immersive, technology-enhanced education. This work contributes to the evolving discourse on IVR in academia, providing actionable insights for educators, policymakers, and developers to optimize immersive learning ecosystems.

Enhancing Traceability in Organic Rice Supply Chain with Blockchain Technology Developed by Design Science Research Methodology

The organic rice supply chain in Indonesia, particularly in Banten Province, is characterized by high complexity and the involvement of multiple actors, which creates challenges related to transparency, traceability, and product authenticity.These issues reduce consumer trust and complicate regulatory supervision in organic farming systems.This study aims to design and evaluate a blockchain-based traceability model to enhance transparency, ensure product authenticity, and support food safety compliance in the organic rice supply chain.This research employs the Design Science Research Methodology (DSRM), encompassing problem identification, objective definition, artifact design and development, demonstration, and evaluation.Data were collected through interviews, field observations, and Focus Group Discussions (FGDs) involving organic rice supply chain actors, government regulators, and experts.The proposed model was empirically evaluated using Partial Least Squares-Structural Equation Modeling (PLS-SEM) based on responses from 220 participants.The resulting Organic Rice-Supply Chain Traceability (Organic Rice-SCT) model integrates farmers, farmer cooperatives, business actors, retailers, consumers, and government agencies within a blockchain-based system supported by quick response (QR) code technology.The findings indicate that operational excellence, cultural suitability, environmental conditions, quality assurance, and organizational resources significantly influence blockchain adoption.Conversely, data management, supply chain integration, technology maturity, and knowledge management show no significant effect.The model demonstrates its capability to improve supply chain visibility, reduce information asymmetry, strengthen regulatory oversight, and support compliance with Fresh Plant-Based Food (Pangan Segar Asal Tumbuhan, PSAT) certification.In conclusion, this study provides a validated blockchain-based traceability model that enhances transparency and trust in organic rice supply chains.Practically, the model supports stakeholders and regulators in ensuring food safety and product authenticity, while theoretically contributing to the literature on blockchain adoption in sustainable agricultural systems.

Hoax News Detection on Social Media: A Survey

Information and Communication Technology (ICT) is a tool to spread and share news effectively. Social media is an Information and Communication Technology product which is a trend of future communication styles, and communication is all about an activity to share the news. The news shared on social media are not always incredible resources, or on the other hand, we can say that most of them are a hoax. According to this condition, research would like to explore what kind of method approach to detect hoax news. This research uses a survey approach to papers published during 2016–2018. By doing this work, we can know the kind of algorithms used for a similar research topic. The most popular approach according to this work is the Classification using Support Vector Machine (SVM), and the most used social media platform is Twitter.

A Framework to Mine High-Level Emerging Patterns by Attribute-Oriented Induction

Building a popular mobile application by utilizing user feedback

Developing a popular mobile application which is suitable to the users based on the input that users have given with their feedback is very challenging. There are many new mobile applications with high user feedback but few download rates. Moreover, some users of the application are unwilling to give their feedbacks. This abrupt state is caused by the carelessness of mobile application developers in noticing the importance of user feedback and user behavior. In this paper, we will state several steps and options that could be taken by mobile application developers to popularize their mobile application. This solution is mainly focused on the utilization of user feedback and user behavior, which also include proper use of feedback loop, great advertising, and behavioral change. The objectives of this analysis are to change unpopular mobile applications with high user feedback to become popular mobile application with high user download rate, as well as to encourage users to properly express their opinion regarding the application by giving out their feedback to the mobile application developers.

Measuring the quality of various version an object-oriented software utilizing CK metrics

There are many object-oriented software quality measurement techniques. One of them is CK metric. This paper utilizes CK metrics to measure qualities per version of an open source software namely Statcato. Measurements are done in each class, each version of the software. The measurement is used to analyze the quality of the software. This paper concludes that the quality of software has improved during its lifecycle, although its features increase.

Critical Success Factor of Information Technology Implementation in Supply Chain Management: Literature Review

The main objective of this research is to explore the success factors of implementation information technology (IT) in supply chain management. The method used is a literature review with meta-analysis approach, meta-analysis used by using keywords that describe the search. The results of this study found 56 success factors, after further analysis found 21 success factors are very influential from the implementation IT, among others: Efficient, Integration, Cost, Effectiveness, Communication, Ability, Timeliness, Accurate, Accurate, Performance, Coordination, Flexible, Automation, Quality, Transparent, Real-Time, Responsiveness, Trust, Speed, Completeness, Relationships, Suitability. This factor gives the company the desire to implement IT in the supply chain. The application of IT has a positive influence on all supply chain activities so it can improve the processes that occur within the company from purchasing raw materials, production processes, logistics, customer service. All this gives the opportunity for a company to compete globally with others.

Success Factors of the Blockchain Adoption for Smart Manufacture

Currently, the discussion about smart city becomes a trend topic in which the development of the Internet of Thing (IoT) is used in various sectors including in the manufacturing sector where the Indonesian government is intensively increasing the capacity and production quality. The use of IoT in both smart manufacture and smart city become an interesting thing topic for research and the combination of the new technology Blockchain, may proof the reliability of data. Trust, transparent, secure, real time online process as a new phenomenon to merge IoT with Blockchain technology into smart manufacturing technology. However, for maintaining quality and sustainability, required technology governance so the achievement of implementation can be done well. This research uses the method called Systematic Literature Review (SLR) find the Blockchain adoption factors for Smart Manufacturing Technology Governance and the research conducted on several reputable publications. The result found, there are 35 adoption success factors found from the adoption of blockchain technology into smart manufacture industry. The influence of the technology governance process in smart manufacture industry increase sustainability, realtime and accurate decision making for the industry.

Mobile Application for flood disaster in Jakarta

Jakarta as the Indonesian capital city has extreme average rainfall in 2020, based on data from the DKI Jakarta Representative Central Bureau of Statistics and the meteorology, climatology, and geophysics agency. From year to year, the city of Jakarta has not been spared from floods which have seriously disrupted the activities of the people of Jakarta. Flood disasters greatly disrupt people's economic activities and cause various other problems including public health problems, one of which is causing infectious diseases. In such a case, this research work proposes a mobile application for flood disaster monitoring that will help the Jakarta government to maintain the flood as real flood information in Jakarta. The Jakarta city government and the residents of the city of Jakarta can work hand in hand using mobile application technology to combat this flood problem and enable the Jakarta city government to quickly overcome any reported flood problems. The proposed mobile application was modeled with a use case and class diagrams.

Estimated measurement quality software on structural model academic system with Function Point Analysis

In the software development indispensable is the suitability and accuracy in determining the size or value of the software to fit the operation to be performed. A wide variety of calculation methods have been widely used to estimate the size of the software, one of which is by using Function Point Analysis (FPA). Volume calculation software based on a scale of complexity. Since the point of measurement is highly subjective, in order to maintain consistency and validity of the results, the method should be run by an experienced professional. This method is then applied by the authors to measure the complexity of academic information system STIKOM Dinamika Bangsa Jambi using structured modeling approach. Measurements were performed in this study consisted of depictions information system is built into the structure. Which is then analyzed by counting models Crude Function Points (CRP), the relative complexity of Adjustment Factor (RCAF), and then calculate the point function. From the results of calculations using the FPA to software quality measurement academic system STIKOM Dinamika Bangsa Jambi obtained value FP 166.32 is good. Function point value produced will be used by developers in determining the price and the cost of software systems to be built or developed.

Comparison of Similarity Coefficients on Morphological Rodent Tuber

Many comparisons of similarity coefficient done by researchers, especially in the field of biology. This comparison aims to find the most appropriate similarity coefficient for some cases. Many results found that Sorensen-dice coefficient and Jaccard coefficient is close or even identical. But Jaccard coefficient can not handle properly for sets with real-value or weighted sets or any pair of vectors. So, Jaccard coefficient redefined as Generalized Jaccard Coefficient. This paper shows the correlation between Sorensen-dice coefficient with Generalized Jaccard Coefficient using Spearman's correlation as predecessors research did and using ANOVA to ensure the results. This research find that the comparison between them is less similar from predecessors research.

Buzzer Detection to Maintain Information Neutrality in 2019 Indonesia Presidential Election

This paper proposed a method which detects a political buzzer in social media, specifically Instagram. With Indonesia undergoing 2019 presidential election, a detection of buzzers that causes much trouble in maintaining information neutrality is seen as a needed. One of the many reasons is because those buzzers spread false news making the information gained by the use of social media to be not neutral and deliberately offends or attack those that they are not in favor of. Those buzzers share a similar characteristic, tendency, or even possess the same pattern. Grouping, classification, and detection method are used to counter this problem. This research gives a slight overview of what is happening in social media and a theory of how to deal with those problems. The argument is expected to help to identify buzzer in real life, thus helps in maintaining information neutrality along with the social media in Indonesia.

Blockchain Technology for Tracing Drug with a Multichain Platform: Simulation Method

This study builds the implementation of the traceability process by conducting simulation tests using business process simulations with the implementation of blockchain technology to track drugs. This research focus involved stakeholders, including the pharmaceutical industry, pharmaceutical wholesalers (distributors/wholesalers), health services (drug stores, hospitals), consumers. Simulation methods are used to describe the distribution and traceability of drugs. Finally, the research contribution in incorporating blockchain technology to supply chain management could potentially help in drug traceability. This study provides an overview of blockchain technology capabilities to find out which stakeholders and assets are transacted on the blockchain system. A decentralized Autonomous Organization is an approach to organizing data on the blockchain that defines all stakeholders identities associated with different addresses. This process can organize each address's transactions on a special blockchain platform in this study using multichain. Furthermore, transactions that have occurred cannot be updated or deleted. This simulation also illustrates some of the blockchain characteristics that must exist, among others, transparent, distributed, immutable, and peer to peer transactions. This contribution gives supply chain management, in particular on drug distribution, stronger control over distribution.

Detepet Mobile Application for Pet Tracking

We have often heard of cases of loss of animals in the world, the owners of these animals are very troubled in looking for lost animals, so often when the pet is lost, the owner leaves it or makes the news of the competition for who can find it. We strive to help these pet owners easily detect the presence of pets they have through a mobile application that we make, namely Detepet that can track all the pets you have, so you are not afraid of losing your pet. In this case, the user should register their pet and the tool GPS necklace will be attached to the pet and the pet will be under monitoring of the systems.

PEMAHAMAN TEORI DATA WAREHOUSE BAGI MAHASISWA TAHUN AWAL JENJANG STRATA SATU BIDANG ILMU KOMPUTER

As a Computer scientist, a computer science students should have understanding about database theory as a concept of data maintenance. Database will be needed in every single human real life computer implementation such as information systems, information technology, internet, games, artificial intelligence, robot and so on. Inevitably, the right data handling and managament will produce excellent technology implementation. Data warehouse as one of the specialization subject which is offered in computer science study program final semester, provide challenge for computer science students.A survey was conducted on 18 students of early year of computer science study program at Surya university and giving hypothesis that for those students who ever heard of a data warehouse would be interested to learn data warehouse and on other hand, students who had never heard of the data warehouse will not be interested to learn data warehouse. Therefore, it is important that delivery of the Data warehouse subject material should be understood by lecturers, so that students can well understoodwith the data warehouse.

Sistem Pendukung Keputusan Penentu Penerima Reward Guru Dengan Metode Weighted Product (WP)

Sumber Daya Manusia (SDM) sangat memberikan peran penting dalam menjalankan sebuah organisasi ataupun instansi maupun sebuah negeri, Institusi pendidikan formal mengenban peran penting sebagai ujung tombak untuk menghasilkan output berupa sumber daya manusia yang bermutu dan berkarakter di masa mendatang. SMKN 1 Kragilan adalah salah satu Sekolah Menengah Kejuruan yang berusaha meningkatkan mutu dan kualitas kinerja Guru. Untuk mewujudkan SDM yang berkualitas, perlu adanya penerapan sistem berupa pemberian reward untuk Guru. Akan tetapi, penilaian kinerja guru diterapkan belum tersistemkan, sehingga penilaian kinerja menjadi tidak efektif dan efisien. Maka diperlukan sistem untuk penilaian kinerja Guru dalam pengambilan keputusan penentu penerima reward guru yang dapat menyajikan informasi dalam pengambilan keputusan. Metode Weighted Product (WP) ialah salah satu dari banyak metode yang diimplementasi dalam sebuah Sistem Pendukung Keputusan (SPK) yang diterapkan dalam berbagai algoritma pemecahan masalah.

Translation Learning Tool for Local Language to Bahasa Indonesia using Knuth-Morris-Pratt Algorithm

During the COVID-19 pandemic time, it is a requirement to deliver online learning since students can not have face-to-face meetings and they depend on gadgets such as a computer, mobile phone, iPad, and laptop to continue their study. One of the subjects is local language learning as this subject is a requirement in some provinces in Indonesia as a gesture concerning local wisdom. However, there is a lack of support for learning the local language since the local languages have a mouth to mouth learning knowledge without any dictionary support. This paper proposed the idea of using the Knuth-Morris-Pratt algorithm to translate the Palembang language to Bahasa Indonesia to help students to learn the Palembang language with the application.

Deaf Helper Mobile Application for Interaction of Hearing Disorders Communities

People with hearing loss in this world have not received much serious attention from the authorities. This makes these sufferers confused in choosing learning media to interact with and isolated from their social environment. This application was created to help people with hearing loss to be noticed and understood from the way they communicate using sign language through the mobile application called Assistant for the Deaf, which has many features such as registration, interactive videos, sign language translator, forums, customer service, library, information, history, events, donations, and shops. The application is designed using use case diagrams and class diagrams modeling the database, and the implementation used Android Studio and MySQL database.

The Benefit and Challenge of Blockchain Technology for Tracing Automotive Component: a simulation test

Supply Chain Management System (SCMS) poses as one of the essential aspects for distributing component, especially in contemporary Automotive Industries. In this line, the tracing process of the automotive part is one of the critical features required in this industry, such as for automotive product improvement and its forensic. Early research reported that the tracing component feature is prone to component counterfeit that could cause financial loss and even loss of life. The recent studies emphasized that new development technology, commonly renowned as Blockchain is believed to have the ability to perform tracing automotive component and minimize the counterfeit. However, the feature of Blockchain technology is to track automotive component found in the literature mostly. In this frame, the biggest challenge is to obtain the evidence of Blockchain Technology implementation for automotive part component tracking. Therefore, this report paper is a part of design science research stages striving to perform the simulation test by using business process simulation method of Blockchain implementation for tracing automotive components. The focus group discussion involved a manufacturer automotive component, several distributors of automotive components, and two of the big three car manufacturers in the country. The purpose is to understand a comprehensive supply chain management process in the area of automotive component distribution. During the test by using simulation test model, revealed several advantages and challenges. Ultimately, Blockchain technology is potentially implemented for tracing automotive component and the merging combination system between SCMS and Blockchain Technology that contributes to developing new robust SCMS.

Recommender System Using Transformer Model: A Systematic Literature Review

Online transactions are significant in the pandemic era. Using online transactions can minimize the risk of physical contact with disease transmission between buyers and sellers. However, with so many choices of items, it becomes challenging for users to decide which item suits their needs. For this reason, the recommender system was created as a handy tool. Recommender systems can help provide ratings, compare with other user data, use personal transaction history, use current events, or combine the above methods. Currently, computer science experts are constantly trying to improve recommender systems. In 2017 a new method emerged that uses transformers as one of the deep learning models. The combination of recommender systems and transformers can process extensive data, create different weights for each input data, and process data without sequentially allowing parallel processing and reducing training time significantly. Many papers in various countries are continuously trying to improve this methodology. In this literature review, we try to analyze the technology used, the dataset used, and the area where the technology is implemented. In this case, we carry out collecting papers, then filtering, classifying and analyzing, and making conclusions.

Software reliability measurement base on failure intensity

Software reliability is an important factor in software quality measurement, which is measured by the probability of an error-free software within the operating period within a given time period and environment. Software reliability measurements are performed at every stage of software development process to evaluate whether the software reliability requirements has been fulfilled. In this paper proposed the new methods to measuring the software reliability based on categorize faults. We use J.D Musa-III failure datasets are divided into 5 modules to measure software reliability using our method. Base on J.D Musa-III datasets we got the value of reliability is 0.7416 or 74%. The software reliability can be measured using this method and the future work is to categorize the failure of the software based on the source of its failure.

Measuring memetic algorithm performance on image fingerprints dataset

Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four\npartial tests and at the last of work we measure all computation time.\n\n\n\n\n\n

Spell Checker for the Indonesian Language: Extensive Review

Abstract— Typographical errors are common in written languages, including Indonesian. It will, however, lead to a misunderstanding of the meaning of the words. Nevertheless, an Indonesian spell checker is still uncommon. Furthermore, no extensive literature review of spell checkers for the Indonesian language has been conducted. This study aimed to present extensive literature on spelling correction in the Indonesian language. The methods used were discovering any literature related to the study topic for the period 2017-2022, applying some keywords, and enforcing inclusion and exclusion criteria. According to the findings of this study, in the previous five years, research on spell checkers has increased, and many researchers from various provinces in Indonesia have used different methods or algorithms to evaluate word errors. Keywords— Indonesian language, Methods, spell checker, extensive literature review, typographical error

A Proposed Supply Chain Model of Blockchain Technology-Based in Automotive Component Industry

The automotive industry has rapidly developed and overrun the market for the last decade. In this context, automotive components or parts are essential factors to manufacture automotive products. Undoubtedly, the supplier that provides the automotive components or inbound logistics becomes a critical party of Supply Chain Management activity. In this frame, the failure of inbound logistics potentially creates a severe implication to the automotive industry in terms of the financial effects, company image to damage the customer. Arguably, the supplier activities encompass the continuity of automotive components delivery, on-time delivery schedule, supplier and customer relationship maintenance, and financial operation. In this point, the problem identifies the distribution of automotive components and stock maintenance. Therefore, this qualitative research is essential to explore a proposed Blockchain Technology model in Supply Chain Management (BlcSCM), especially in the automotive component industry. On that basis, design science research methodology facilitates to create a proposed model, Leavitt Diamond. In this model, the essential factors (people, process, technology, and organization) merges with supply chain management. Finally, it refers to the Blockchain Technology model in Supply Chain Management.

Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis

The growth in Information Technology (IT) jobs is predicted to reach 15 percent between 2021 and 2031. The growth of IT jobs has resulted in a remarkable change in all infrastructure, such as information, skills, and domains covered in IT job profiles. Unfortunately, job roles and skills in this field remain undefined. The gap between the supply and demand needs in the IT workforce must be filled immediately with an appropriate strategy. To fulfill industry needs, an in-depth analysis of IT job profiles is important. Therefore, it is important for educational programs to identify the competencies needed by the industry to update their output. This study aims to identify the job profiles required for IT job specialists by analyzing real-world job posts published online to identify hidden meanings from a textual database. A systematic semantic methodology was proposed using an average-linkage hierarchical clustering analysis. It resembles a tree structure technique to discover relevant phrases, relationships, and hidden meanings through semantic analysis. Occurrences of the most frequent words and phrases were extracted to reveal the domain knowledge of each IT job cluster. The result is a systematic semantic analysis of the IT job profile comprising the programming language, specialized type, duty, database, tools, and frameworks. The justification for each job profile was validated by 10 IT professionals from various private and government companies in Indonesia through Focus Group Discussions (FGD).

PEMODELAN ELEARNING PERGURUAN TINGGI DENGAN MENGGUNAKAN FRAMEWORK LEARNING TECHNOLOGY SYSTEM ARCHITECTURE (LTSA) DAN UNIFIED MODELING LANGUAGE (UML)

Saat ini, perguruan tinggi sebagai motor pencerdas bangsa dituntut untuk lebih melek dengan perkembangan informasi teknologi, perkembangan peralatan komunikasi dan jaringan internet berkecepatan tinggi saat ini. Pendidikan di perguruan tinggi dituntut untuk mampu mengarahkan mahasiswa untuk lebih mandiri dan mampu menggunakan teknologi pembelajaran elearning, yang pada akhirnya akan mempengaruhi kemampuan mahasiswa ketika bekerja dan terjun ke dalam masyarakat. Pengembangan sebuah Elearning sebagai sebuah software aplikasi dapat dilakukan dengan berbagai macam metodologi atau framework dan salah satunya adalah framework Learning Technology System Architecture (LTSA) yang merupakan standar 1484.1-2013 dari Institute of Electrical and Electronics Engineers (IEEE) untuk teknologi pembelajaran. Standar IEEE 1484.1-2013 dikembangkan oleh IEEE Learning Technology Standards Committee (LTSC) yang merupakan bagian dari IEEE Computer Society dan diterbitkan pada tahun 2013. Pengembangan elearning dengan framework LTSA akan menerapkan atau memetakan komponen proses dan penyimpanan data pada LTSA dengan menggunakan metode pengembangan berorientasi obyek yang disebut Unified Modeling Language (UML). Penerapan framework LTSA dalam mengembangkan elearning dibatasi pada penggunaan beberapa diagram pada UML seperti sequence diagram, use case diagram, class diagram, package diagram, activity diagram dan component diagram. Penggunaan UML pada pengembangan elearning dengan menggunakan framework LTSA diharapkan dapat memberikan pencerahan bagaimana membangun sebuah elearning dengan paradigma berorientasi obyek. Beberapa contoh penerapan diagram UML dalam pengembangan elearning diberikan sebagai gambaran bagaimana sebuah elearning dikembangkan dengan framework LTSA. Pada akhirnya perguruan tinggi yang mengabaikan teknologi dalam proses pembelajaran akan tidak menarik dan ditinggalkan oleh masyarakat yang semakin peduli dan menikmati teknologi sebagai bagian dari kehidupan manusia.

Understanding of data mining in computer science learning from PILKADA DKI Jakarta 2017

DKI Jakarta gubernatorial election 2017 was phenomenal event which draws attention worldwide, where one of the defeated candidate Ahok or recognized as Basuki Tjahaja Purnama was charged with blasphemy, has double minority background in Indonesia as descendants of China and Christian and he was titled “Man of the Year” 2015 by Globe Asia magazine. However, in this paper we do not interested with political conversation, how one candidate couples can win or how one candidate couples could lose. We are interested to learn based on our science knowledge in Computer Science perspective particularly with the term of Data Mining with this 5 years DKI Jakarta event program. Hopefully, by reading this paper the students of Computer Science can understand their Computer Science knowledge in their surrounding life events. Obviously, voters' decisions are influenced by human nature characteristic which like to similarity preferences, where human like to choose someone who have full or frequent similarity in profile and/or program. Beforehand, voters should do learning by doing the characterization of their and candidate couples' profile and program. Afterwards, based on candidate couples' profile and program, the voters will do discrimination between candidate couples, classification, clustering or association among candidate couples. Admittedly, the voters will fill their ballot based on their preference similarity.

Perancangan Data Warehouse Penjualan (Studi Kasus Pt. Subafood Pangan Jaya)

Penjualan merupakan suatu hal yang penting bagi keberlangsungan hidup suatu perusahaan. Kegiatan penjualan merupakan kegiatan utama perlu untuk dikelola secara baik agar perusahaan tidak selalu mengalami kerugian. Pada penelitian ini penulis akan menoba untuk melakukan perancangan Data Warehouse yaitu sebuah lokasi penyimpanan data yang mempunyai ukuran yang sangat besar, bersifat historis dan dapat dipergunakan untuk memberikan dukungan kepada pimpinan perusahaan dalam mengambil keputusan. Dalam penelitian ini juga akan merancang sebuah Data Warehouse sebagai repository penjualan yang diterapkan menggunakan Pentaho Data Integration Software. Hasil dari Penelitian ini adalah Sebuah rancangan Data Warehouse yang dapat digunakan sebagai repository data-data penjualan.

Web-Based Application for Freelance Tailor

IT Blueprint for an Effective Online Learning System with a Blended Approach for Upper Secondary Education System During COVID-19 Pandemic

Online learning is becoming the main teaching method since the COVID-19 pandemic. What happens after the pandemic is over? Will the online education be still going? The schools have implemented online learning systems to replace the current teaching methods to ensure the teaching and learning process keeps going. However, research have found that not all students respond well to fully online learning methods, therefore a blended learning system is needed to facilitate the difference in student performance. This IT blueprint will ensure that an upper secondary school is equipped with the proper IT system for its purposes as well as propose blended learning system where the school will apply both online learning and face-to-face teaching method at the same time to maximize student potential.

Markov Chain Method in Calculation of Personnel Recruitment Needs

The selection of military personnel through the recruitment stage must be under planning. The calculation in this study uses the Markov Chain method with an error calculation which is the result of a comparison with the conditions of the number of recruits in the previous period so that the error value is known and can be used in the next period using a computerized system. This study provides an alternative to determining the error value in predicting the number of recruits more easily and based on previous data by considering various aspects, such as rank, expertise, and job criteria required.

Trend Intelligent Tutoring System 2018-2022 : Systematic Literature Review

ITS is a method of teaching that uses technology to give lessons without the need for an instructor to be there. A systematic literature review (SLR) was conducted in this research to identify the components of an intelligent tutoring system, and the results are presented. The method used in this literature review is the prism method. The steps in the prism method are slr protocol, search for literature, select literature, summarize evidence and disseminate results. The slr protocol stage, it consists of determining a research question, searching for scientific reference sources with the keyword intelligent tutoring system, then sorting out the appropriate articles and making final conclusions. This article gives a detailed look at how the intelligent tutoring system changed over time during the pandemic epoch. Our contribution is complete information on the components of ITS in times of epidemic at universities, with the goal of improving their learning and practical use of the technology.

Improvement of Steganography Technique: A Survey

The improved technology in information security, now day has been being still developed. This, cause of the object called data which still has an important role in communications. There are two kinds of security technique, they are called steganography and cryptography. This paper will discuss growth of steganography technique from 2015 to 2019. The data obtained from journals and identified as a systematic literature review. The results are that improvement in steganography still developed by modifying the algorithm, combining the method and threating at parallel processing. Future, utilization of steganography is still needed, considered that communication channels are growing fast and sophisticated.

Development of Web Application based on ITIL – Incident Management Framework In Computer Laboratory

While Information Technology becomes the interesting topic in education, Computer Laboratory as a learning center becomes an important supporting facilities in Higher Education. Whereas, current awareness level of service quality for higher education management is quite lower than the enterprise management. In the daily operation of IT Service Management (ITSM) in Computer laboratory, service activities in computer laboratory mostly uncontrolled and not standardized. By maximizing the service standard level in computer laboratory, university could maintain customer satisfaction subsequently. This research will focus in creating an IT Service Model, Service Standard Process, and the web application by referring to ITIL-Incident Management Framework as a best practice of ITSM framework. The design process use UML diagram as the tools in drawing the process flow of system.

Sentiment Analysis of Big Cities on The Island of Java in Indonesia from Twitter Data as A Recommender System

Text mining is a data mining technique to find hidden things from a set of data in the form of text. One of the things that can be obtained with text mining is opinion or sentiment, whether it is positive or negative. Positive sentiment is used as a reference for a subject or object from a collection of texts to be recommended. Java Island is the city with the most population in Indonesia with a variety of culinary delights. This study aims to analyze sentiment recommendations on culinary data from food or cuisine in big cities on the island of Java. Four big cities were selected, namely Jakarta, Bandung, Yogyakarta, and Surabaya. The data source is a tweet of culinary arts of the four cities from Twitter in Indonesia. The Sastrawi Library is used as a text mining data processing tool in Indonesia. The results obtained are the majority of positive sentiments from all cities. The cumulative sentiment value of the four cities is 54%, meaning that the big cities on the island of Java have good culinary delights and deserve to be recommended to be enjoyed.

The Implementation of E-money in Mobile Phone: A Case Study at PT Bank KEB Hana

Innovation in cashless payment instruments can lead to complications in the use of quantity targets in monetary control. The empirical study found that the presence of non-cash payment instruments using cards can replace the role of cash payment instruments in economist transactions in Indonesia. The growth of electronic money when viewed on a monthly basis is much faster than the growth of debit and credit card cards, a monthly increase of electronic money can reach 70% −100%, while debit and credit card cards only grow in the range of 20%. This study aims to analyze the design of e-money, as well as provide some development ideas that must be done related to the implementation of e-money.

Mobile cloud game in high performance computing environment

Mobile cloud game is a solution to play high-end games in indigent thin clients with a diversity of end-user devices, and as real-time gaming, mobile cloud game hosting game engines in the cloud. Moreover, frequent change in network quality is another issue that should be limited to run the real fast cloud game. Thus, reliable software components between cloud and user devices as clients, including using artificial intelligence (AI) algorithms such as machine learning, deep learning and so on will enhance the game performance, particularly in multiplayer and real-time conditions. In this paper, we list the mobile cloud game architecture in the high-performance computing (HPC) environment, where a load of the game will be distributed between servers as cloud and clients. The server node as clouds or clients will consist of more than one server with many processors (cores) or sometimes can be recognized as distributed computing. Using HPC for cloud games will boost the game performance where the execution times will be dispersed not only in some node in servers and clients but in many cores of each server or client. The involvement of the internet of things (IoT) and ubiquitous access from heterogeneous devices will give benefit to enjoyment in the game itself.

PENGEMBANGAN LEARNING CHARACTERISTIC RULE PADA ALGORITMA DATA MINING ATTRIBUTE ORIENTED INDUCTION

This paper shows the improvement of current characteristic rule learning in Attribute Oriented Induction (AOI) data mining technique. The proposed algorithm was applied with improvement upon current algorithm with 3 steps where the first step is elimination for checking condition if there is no higher level concept in concept hierarchy for attribute. The second step is elimination of attribute removal if fulfill for checking condition if there is no higher level concept. The third step is elimination of attributes in input dataset which no higher level concept in concept hierarchy. The development of these data mining algorithm applied Knowledge Data Discovery (KDD) methodology which consist 7 steps. Current and proposed AOI characteristic rule learning were implemented with server programming such as PHP Hypertext Preprocessor (PHP) and using 4 input datasets such as adult, breast cancer, census and IPUMS from University of California, Irvine (UCI) machine learning repository. The experiments showed that proposed AOI characteristic rule are better than current AOI characteristic rule, where experiments upon adult, breast cancer, census, IPUMS datasets have average 11, 3.8, 7.2, 7.2 respectively times better performance. The experiments were carried on AMD A10-7300(1.90 GHz) processor with 8.00 GB RAM

Survey of emerging patterns

Emerging patterns (EPs) which found in 1999 has been proven as strong discriminator which strongly describe significant between 2 datasets. As strong discriminator, EPs will be interested to be used, applied and mixed in many algorithms for finding patterns in many different datasets particularly for text datasets. Using EPs algorithm in Database Management Systems (DBMS) such as MySQL, SQLServer and etc will be interested as well and need to be explored. The differences between 2 datasets literally discriminate knowledge between those datasets which represent with growthrate number as justification of EPs. Moreover, confidence of EPs can be measured in order to secure of finding EPs where confidence will have 100% as maximum score. Since the discrimination is not only between 2 datasets then EPs algorithms have been extended to discriminate between more than 2 datasets which recognized as EPs classification and there are many EPs classification algorithms including Jumping EPs classification as well.

Indonesian's Dangdut Music Classification Based on Audio Features

The uniqueness of modern dangdut music today is in the beat and melody of music that is relatively faster like techno dance music, and the progressive melody arrangement from synthesizer keyboard. In this paper, we will extract audio features from music, and then used that features for classification based on machine learning methods. Support Vector Machine (SVM) was used in this study. In this paper's experiment we use 32 audio music files, consisting of 16 for dangdut music and 16 for techno dance music. The results of this testing get varying accuracy levels, between 80% to 90% for each music audio file that has been successfully classified.

E-payment for Jakarta Smart Public Transportation, Using the Point System for E-Commerce

Abstract The ease of using transportation is one of the most critical things in the city with a significant population like Jakarta. The growth of the population in Jakarta is increased rapidly. The wage that Many transportations are causing a traffic jam in Jakarta. The government suggests that people use public transportation for their mobility. However, people choose to use their vehicles rather than public transportation. The main reason is that public transportation cannot guarantee the arriving time, whether it is on time or not. Many people move on to online transportation services. However, the massive growth of online transportation is still a contradiction as public transportation. People in Jakarta need faster mobility to go. This research is trying to make a system for public transport without any delay and hard to use it. This problem can be solved by building a new system. This system required e-wallet for payment in public transportation. This E-Wallet will search the possible route from the nearest location to the destination — only the public transport where there is a station that can use it. By using QR-Code generated by e-wallet on the mobile phone, people can scan it directly to a machine located in the station. This method will make a transaction faster than ever. Moreover, people can enjoy another feature like cashback and redeem the prize by a point system. In this era, e-commerce is dominating the market for purchasing something. This is also the attraction of the system. This system is also required excellent facilities from the government so that people can enjoy it.

Facial Recognition Development to Detect Corporate Employees Stress Level

Technology has developed so well even to the point where machines can recognize our faces. There many APIs and algorithms out there that can recognize human faces. These APIs or algorithms is called Artificial Intelligence or AI. As modern civilian life in modern society, AI has been used so many times and developed into something more. This time, we proposed an idea to recognize motions and emotions on human faces to detect their stress levels. Companies nowadays need to have something that can know how their employees are feeling or think about their work. This program works by taking an image input from mounted cameras on the office&#x2019;s monitor. However, before that, the program must have been trained first to recognize faces and emotions. For this, the eigenfaces algorithm is used. After the input is received, then the program will process the image received and extract the features from the images such as eyes, nose, mouth, etc. to detect if there are faces and also the emotions of the face. After that, the features extracted will be matched with the features in the database to give an output of the percentage of stress.

Application for Easy Organizing of Event Organizer

These days, Indonesia has entered the era of Industry 4.0 which is marked by the development of all-digital technology and automation. With the increasing rate of technology development that changes people's lives, people are becoming fond of anything fast and instant. With the help of these technological advancements, we create an application that makes it easier for users that are willing to hold events. This application connects 2 entities, which are users and an event organizer. The event organizer can easily find their market and other hands easy as well for the user for finding the market for event organization activities. The business processes in this application were designed with a use case diagram, and the tables in the database were designed with the class diagram and the menu of the application with User Interface (UI). However, this proposed application just only an introduction and needs to be explored for those who are interested to explore entrepreneurship in these areas.

A literature review of crowd-counting system on convolutional neural network

Abstract With the proliferation usage of video surveillance for safety, traffic control, and privacy purposes and with the constant growth of population, it is important to keep monitoring using Closed-Circuit Television (CCTV). With new upcoming developed technologies, new systems and algorithms are introduced and implemented to the crowd counting system today retrieving live video surveillance from the CCTV. However, recent studies show that there are some challenges still faced regarding the crowd counting system which uses the density estimation. The problems that occurred have resulted from the inaccuracy of the system that is caused by several factors. Factors such as the perspective distortion which is caused by the lack of data training and the method such as face detection is an ineffective method to determine the population density. Studies proposed have projected the idea of developing a more robust crowd counting methodology by implementing crowd counting by detection, clustering, and regression. Implementing these methods using the Convolutional Neural Network (CNN) will better the result of the detection since in CNN the image can be inputted and it will undergo several layers which will result in the system being able to differentiate one image from the other. With CNN the process of crowd counting will be able to be more advanced.

The influence of data size on a high-performance computing memetic algorithm in fingerprint dataset

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.

Mobile Application for the Blind and Their Family

Many people want to use gadgets, but they cannot due to some reasons. One of the reasons is the person who is visually impaired and some children are challenged by birth. To overcome this problem, we have designed a mobile application to make communication easier between the blind and their family or their friends. In this project, we want to utilize some of the technology that already exists to develop a mobile application that can be used by the blind to communicate easily to their family.

Sustainability Psychology of Disruption: Attitude towards Water Purification Technology Can Be Predicted by Cultural Value Orientation and Personality Traits

The clean water crisis, particularly drinking water, is an issue that is strongly tied to water sustainability. The availability of disruptive technology that is cheaper and easy to use, such as a water purifier, is one answer to the ongoing crisis. Unfortunately, a disparity exists in the attitudes of Indonesia’s society toward this technology. Five cultural orientations are set as predictors, each hypothesized as able to predict such attitudes as mediated by relevant personality traits. This study applied a correlational-predictive design toward 244 individuals (112 males and 132 females, Mage = 23.766 years old, SDage = 6.196 years) residing in Jakarta, Indonesia’s capital city. The main results were: (1) uncertainty avoidance can predict attitudes toward water purification technology through the conscientiousness trait; (2) power distance is unable to predict attitudes through neuroticism; (3) collectivism can predict attitudes through agreeableness; (4) masculinity is unable to predict attitudes through extraversion; and (5) long-term orientation can predict attitudes toward water purification technology through the openness trait. Prior studies have generally employed culture and personality as two separate predictors, yet this study might be the first in setting culture and personality as socio-psychological processes that shape a person’s attitude toward water purification technology in a single theoretical model.

Comparison of Binary Particle Swarm Optimization And Binary Dragonfly Algorithm for Choosing the Feature Selection

Artificial life is a living behavior that comes from animals and humans which is currently used as inspiration in making algorithms and is usually used to look for patterns such as classification, feature selection, optimization, and many more. Binary Particle Swarm Optimization (BPSO) and Binary Dragonfly Algorithm (BDA) are algorithms derived from Artificial Life and Genetic Algorithms. The purpose of this research is to compare Binary Particle Swarm Optimization (BPSO), Binary Dragonfly Algorithm (BDA). The dataset used is breast cancer from the UCI public dataset. The results of the feature selection will be used in the K-NN (K Nearest Neighbor) classification algorithm to get the Accuracy, Precision, Recall values. The BPSO algorithm is still superior to the BDA algorithm because the iteration process of BPSO algorithm is more centralized in solving existing problems. the BPSO algorithm produces 97.5% accuracy, 97% precision, and 98.98% recall compared to the BDA algorithm whose value is still below BPSO.

Sharing Food with FoodLifeSavr Smartphone App

In this era, people often overeat, make parties or events, where there are excessive availability of food. As well as shops or restaurants that sell food, has ready-to-eat foods that only lasts one day and then treated as expired food. Often this precious food is thrown in the trash, and meanwhile, many destitute people cannot eat enough per day. Therefore, this research paper aims to mobilize people who overeat and donate their excess food that can still be consumed to those in need. This smartphone application is equipped with activities such as registering for new donors who want to join the mobile application and logging in for donors who are already registered in the mobile application. Donors can monitor couriers taking photos of recipients with food donated from donors. In addition, donors can select the food category for donation and input amount, track the donated food that the courier has picked up, and send it to the recipient. This mobile application is also equipped with donors to share experiences, ideas, and views based on topics created by staff and available threads.

The Use of Deep and Machine Learning for Face Expression Recognition: A Literature Review

In the current era, Artificial Intelligence (AI) and Computer Vision take the main role and participating to the people daily activities. Face Expression became as interesting topic to be explore. Face expression recognition or detection could be applied in many aspects, such as for student focus detection based on face expression, intruder detection system, lie detection and many more. Referring the useful of face expression detection, there are also much research that focused on the methodology to detects or recognize the face expression. Several approaches are use such by using Deep Learning, Machine Learning and statistical method. This paper focused on the process to obtain and knowing the best approach or methods to recognize the face expression by using Systematic Literature Review (SLR) process. From hundreds of retrieved papers with similar interest, we do the selection based on the interest, scope, and topic similarity. The process keeps repeating until we found papers that has similarity criteria in output, dataset, subject and the methods. There are 6 papers in total that fulfil our requirements based on subject, output, result and methods. Subsequently, every research paper was deep-reviewed and compared with another to find the best approaches proposed and dataset used.

TATA KELOLA DATABASE PERGURUAN TINGGI YANG OPTIMAL DENGAN DATA WAREHOUSE

The emergence of new higher education institutions has created the competition in higher education market, and data warehouse can be used as an effective technology tools for increasing competitiveness in the higher education market. Data warehouse produce reliable reports for the institution's high-level management in short time for faster and better decision making, not only on increasing the admission number of students, but also on the possibility to find extraordinary, unconventional funds for the institution. Efficiency comparison was based on length and amount of processed records, total processed byte, amount of processed tables, time to run query and produced record on OLTP database and data warehouse. Efficiency percentages was measured by the formula for percentage increasing and the average efficiency percentage of 461.801,04% shows that using data warehouse is more powerful and efficient rather than using OLTP database. Data warehouse was modeled based on hypercube which is created by limited high demand reports which usually used by high level management. In every table of fact and dimension fields will be inserted which represent the loading constructive merge where the ETL (Extraction, Transformation and Loading) process is run based on the old and new files.

KNOWLEDGE MANAGEMENT AND SOCIAL LEARNING INTEGRATION: A CONCEPTUAL MODEL FOR HIGHER EDUCATION

MYANIMACH – Aplikasi Mobile Untuk Membantu Binatang Yang Diabaikan

Currently, there is no specific platform to manage the adoption process for stray animals in Indonesia. This paper describes the design of a Mobile Application created to address this issue in Jakarta, named myAnimach. This app allows strays and potential adopters to meet their needs easier. myAnimach provides a user-friendly, generic user interface to help them find the strays that meet their criteria or make a post about strays in their neighborhood. Users who want to adopt or offer animals to be adopted can log in or create an account on myAnimach. If the user is interested in becoming an adopter, they need to fill the adoption form. There are several stages in the adoption process. The completion of all the stages will result in an issuance of an adoption letter from myAnimach. Aside from adopting animals, users can also look for adopters for their animals by filling out the upload form on the mobile application.

Online Game Marketplace for Online Game Virtual Item Transaction

This paper trying to create something new regarding the online game marketplace for producing an integrated marketplace with several combined genre and categories of games. The primary purpose of this research is to develop e-commerce for online games and explain the benefit that people can get from it. This research focuses on building an online marketplace based on a website that can provide an opportunity to do it transaction related to a specific game. Mainly, the online games marketplace quality will be based on user satisfaction and user recommendation from the previous research. Meanwhile, these satisfaction level include four dimensions that decide the perceived value of the user reason for purchasing an online games item, which are functional value, emotional value, monetary value, and social value. From that information, we build a marketplace for online games item to satisfy the user requirement and also benefiting with its advantages. We also provide the difference between e-commerce mainly with our proposed e-commerce for online games item. The results will be a full-fledged web-based application for online games item marketplace. This paper will be an opportunity to further development of online games marketplace for the growth of online games transaction.

Malware Detection using Hybrid Autoencoder Approach for Better Security in Educational Institutions

The development of malware that continues to increase is one of the severe challenges experienced by various sectors, especially in the education sector. Educational institutions that provide open networks and connected to many devices simultaneously become an exciting testing ground for cybercriminals. Anti-virus is often used as prevention by applying signature-based detection of specific tiles contained in the database. Unfortunately, because much new malware continues to grow, the detection of the database becomes inaccurate. Therefore, automatic detection has been carried out using Autoencoder hybridized with ANN and CNN approaches. It will be developed using Python programming language with computer specification of 16GB RAM and Processor i7-7500U. Samples used are malware dataset obtained from various types of repositories such as virussign.com, the Zoo, and certain libraries from researchers. The dimensions of samples will be reconstructed using Autoencoder to generate new values that will be used as input on ANN and CNN. This research also has been developed Principal Component Analysis as a comparison architecture. Those approaches provide outstanding results in detecting malware, scilicet PCA-ANN with an accuracy of 0.985, PCACNN with an accuracy of 0.963, Autoencoder -ANN with an accuracy of 0.994, and Autoencoder -CNN with an accuracy of 0.970. Therefore, those approaches can be used as reference in increasing the need for educational institutions to improve their security from malware by detecting tiles that will be sent or read by each user.

Jakarta Smart City Mobile Application for Problem Reporting

This paper discusses the possibility of a better smart city implementation with the Jakarta Information App. Our App will be a one-stop go platform where people could access new information regarding the city live and instantaneously, the information contained is not just from an average news report, but people from social media or communities can also contribute in giving up to date news 24/7. In order to create this app, some processes are done to dive deeper into Jakarta's core infrastructure, the way of living, and the society behind the city. Jakarta Information App's goals are to connect the people everywhere at any time or place; it is not just an average social media app, its main objective is to be some hub for the city, it is an a2-way connected dots, the city with the people and also the reverse. The proposed system has been modeled with a use case diagram to design the business process and using the class diagram to model the database model diagram. The user interface has been designed as a means of communication between the user and the system.

Mobile application to track people in covid19 monitoring and patients under covid19 supervision

Abstract It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients.

Response Time Assessment of Hilbert Huang Transform and Prony Analysis Using Phasor Measurement Data

MINING SIMILAR PATTERN WITH ATTRIBUTE ORIENTED INDUCTION HIGH LEVEL EMERGING PATTERN (AOI-HEP) DATA MINING TECHNIQUE

AOI-HEP (Attribute Oriented Induction High Emerging Pattern) as new data mining technique has been success to mine frequent pattern and is extended to mine similar patterns. AOI-HEP is success to mine 3 and 1 similar patterns from IPUMS and breast cancer UCI machine learning datasets respectively. Meanwhile, the experiments showed that there was no finding similar patterns on adult and census UCI machine learning datasets. The experiments showed that finding AOI-HEP similar pattern in dataset is influenced by learning on chosen high level concept attribute in concept hierarchy and it is applied to AOI-HEP frequent pattern in previous research as well. The experiments chosed high level concept attributes such as workclass, clump thickness, means and marts for adult, breast cancer, census and IPUMS datasets respectively. In order to proof that the chosen high level concept attribute will influences the AOI-HEP similar pattern in dataset, then extended experiments were carried on and the finding were census dataset which had been none AOI-HEP similar pattern, had AOI-HEP similar pattern when learned on high level concept in marital attribute. Meanwhile, Breast cancer which had been had 1 AOI-HEP similar pattern, had none AOI-HEP similar pattern when learned on high level concept in attributes such as cell size, cell shape and bare nuclei. The 2 of 3 finding Similar patterns in IPUMS dataset have strong discriminant rule since having large growth rates such as 1.53% and 3.47%, and having large supports in target dataset such as 4.54% and 5.45 respectively. Moreover, there have small supports in contrasting dataset such as 2.96% and 1.57% respectively.

Software size measurement with use case point for employee application software at STT-PLN

Employee Application (EA) is a system information for realizing the latest personal data and integrated, provide accurate employee information for planning, development, welfare and control of employees. EA in STT-PLN began to be published since 2013. The software size of the EA STT-PLN will be measured with use case point method. Measurement of the software size of EA STT-PLN will be measured with Use Case Point upon use case diagram for EA STT-PLN as shown in the project has small software size where score Use Case Point (UCP) = 70.34. UCP is another alternative implementation method which can be applied to measure application software size whenever needed to deal with time, money and people.

Genetic variation of somaclonal mutants from the 8th generation of Pekalongan accession rodent tuber (<i>typhonium flagelliforme lodd</i>.) based on RAPD-PCR analysis

Rodent tuber is a potential plant which can be developed as an anticancer drug. The rodent tuber plant has a low anticancer compound which does not have an economic value to be utilized on an industrial scale, particularly to explore as an anticancer drug. The material plant was used 30 clones of Rodent Tuber Pekalongan to increase its bioactive compounds using a combination of in vitro culture with gamma-ray irradiation. The selection process has been done until the 8th generation and obtained 14 somaclonal mutants that have higher bioactive compounds than control plant (non-mutant). This study aimed to analyze the genetic variation of 14 somaclonal mutants of rodent tuber using RAPD with 14 primers. The results were produced 513 total bands with the size of 200-5000 bp and 380 polymorphic bands between somaclonal mutants. OPB-18 primer produced the most polymorphic bands and five specific polymorphic bands of 1336 bp, 1070 bp, 901 bp, 861 bp and 728 bp in the somaclonal mutant M23 (20-4-2-1-1-1). OPD-20 primer produced seven specific polymorphic bands of 1574 bp, 1557 bp, 1501 bp, 1496 bp, 1234 bp, 1229 bp and 1086 bp in the M23 somaclonal mutant. The phylogenetic analysis showed that there were two main groups with the coefficient similarity between 0.77 and 0.83. The highest genetic variation obtained was found in the M23 somaclonal mutant with a genetic difference of 23% compared with control. There are five somaclonal mutants (M24, M22, M21, M16, M14) that have a genetic variation of up to 22% over the control. This study shows that OPB-18 and OPD-20 primers were efficient in detecting somaclonal mutant variants.

The architecture social media and online newspaper credibility measurement for fake news detection

Social media is one of the communication media favored by people in the world, especially in Indonesia. This is evidenced by the results of the APJII survey which shows that the majority of Indonesians use social media in their daily activities. One of the advantages of social media is the dissemination of information faster than conventional media so that the quality of information disseminated is lower than conventional media due to the process of disseminating information not through the filter process. By measuring the level of credibility of the online newspaper based on the time credibility, website credibility, and message credibility factors and measuring the level of credibility on social media based on the time credibility, Social Media Credibility, and Message Credibility factors with different levels of weight, it will produce a news likelihood level it's fake news or facts.

Web Security and Vulnerability: A Literature Review

Abstract The web continues to grow and attacks against the web continue to increase. This paper focuses on the literature review on scanning web vulnerabilities and solutions to mitigate web attacks. Vulnerability scanning methods will be reviewed as well as frameworks for improving web security. This research is the basis for future work that will end with the elaboration of web scanning and security with the aim of proposing better innovations.

Disrupting Money: Psychological Factors of Investment Biases in Cryptocurrency

The presence of cryptocurrencies as means of trading and investing has presented new opportunities to make profits. Unfortunately, investment bias becomes a phenomenon that accompanies investing behavior for many people which actually results in losses and regrets. This study uses a narrative review method to identify cognitive, affective, and contextual factors that correlate with investment biases in cryptocurrency. The results of the review indicate that a number of factors - i.e. self-affirmation, anticipation of postdecision dissonance, fear of missing out (FoMO), overconfidence, perception of the investment process and regulation - play a pivotal role in explaining investment biases in cryptocurrency.

Mobile Application for Children to Learn BISINDO Sign Language

Sign language is the most commonly used communication medium for people who are deaf and mute. By using body movements, they can convey meaning through visual cues, not vocal cues. This type of language is learned almost exdusively by people who are deaf or dumb and those who interact with them on a daily basis. Most people do not learn it because sign language is not part of basic education, and the lack of need to use it, especially from a young age. Most of the spoken languages in the world usually have sign language variants according to the language of their respective countries. In this study, a mobile application is developed for children to learn Indonesian sign language where the process model is designed using use case diagrams, and the database design is modeled with class diagrams. As for the implementation, the Android Studio software is used for the application, and the MySQLdatabase is for database storage.

Easy understanding for mining discriminant itemset with emerging patterns

This paper will give basic knowledge understanding how to discriminate between two datasets with Emerging Patterns (Eps) upon famous weather dataset. This paper didn't use previous data mining techniques such as border-based algorithm or so on, but only to give the systematic basic knowledge understanding to discriminate between two datasets by finding score of support, growthrate and confidence. The discrimination will give example to differ between two datasets with itemset/pattern which consist one attribute/dimension or multidimensional attributes and of course by finding Jumping EPs (JEPs) as strong discrimination with proofing of their finding growthrate with confidence score. The finding discrimination shows as uninterested or interested discrimination when having confidence below or greater than 50%. These discrimination results can be used for classification purposes as well.

Measurement Metric Proposed For Big Data Analytics System

Big data is defined as a very large data set (volume), velocity and variety. Big data analytics systems must be supports for parallel processing and large storage. The problem of this research is how to identify measurement metric based on big data analytics system characteristic. One device that support big data platform is Hadoop. Measurement is a process for assigning values or symbols to the attributes of an entity. The purpose of measurement is to distinguish between entities one to another. Indicator for software measurement represented with a metric. The aim of this research is to proposes some measurement metric for big data analytics system. This research using UML exactly a class diagram in system modelling to identify the measurement metric. Both of dynamic and static metric is proposed as solution to measure big data analytics system. Result for this researh are some measurement ndicator both of dynamic and static metric based on class diagram for big data analytics.

Aplikasi Deaf Helper untuk Interaksi Komunitas Penderita Gangguan Pendengaran

Penderita gangguan pendengaran belum mendapat perhatian serius dari pihak yang berwenang, yang membuat para penderita ini bingung untuk memilih media pembelajaran untuk melakukan interaksi dan diisolasi dari lingkungan sosial mereka. Kami berusaha untuk membantu orang-orang dengan gangguan pendengaran dan bagaimana berkomunikasi menggunakan aplikasi mobile yang disebut Deaf Helper, yang memiliki banyak fitur seperti pendaftaran, video interaktif , penerjemah bahasa isyarat, forum, layanan pelanggan, perpustakaan, informasi, sejarah, acara, donasi, dan toko.

Comparison of Xbox One and Steam Joystick-based Operating System User Interface using KLM- GOMS

Despite having many advances in video game control such as voice or gesture control, most video game console manufacturers still use a joystick as the main control for their console. Currently, the problem with video game manufacturers is that instead of using the joystick as the point of user interface design, they instead used a design from another device such as a computer or tv. The research will compare Xbox One and Steam OS user interface by using KLM-GOMS Model. Based on the calculation it is concluded that the overall Xbox One has a more efficient design compared to Steam.

Semantic Literature Review on Non-Fungible Token: Expansion Area of Usage &amp; Trends

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.

Data Warehouse Design for Firefighters Operational at the DKI Jakarta Fire Department

This paper proposed two models of data warehouse schema for the fire department of DKI Jakarta, where the 1st model contains six tables consisting of 3 fact and 3-dimensional tables, and the 2nd model only contains three fact tables. The 2nd model denormalises the 1st model, where the number of tables is less than the 1st model, where at the end of the day, the 2nd model will reduce the join table process, which increases the SQL performances. These two models have been recognised as fact constellation schema with more than one fact table and sharing dimension and sub-dimension tables. The database resources were collected from http://data.jakarta.go.id under the Fire and Rescue Service Agency. Those two data warehouse schema models were developed based on a report sector list, a report on Hydrants list, and vehicle register reports. This paper proposes to support Automatic Identification Systems (AIS) research, particularly implementing the data warehouse concept.

The Development of Intelligent Tutoring System in The Military Schools

The rapid and fast development of technology affects many aspects of life, especially in the field of education. The application of technology in education is needed to support a more effective and efficient learning process without any space and time constraints. An example is the implementation of the Intelligent Tutoring System (ITS) which is often known as an intelligent learning system. An ITS in the field of military education is needed to produce professional military graduates/HR. Therefore, researchers will examine trends in research development and updates on ITS technology in the military field. Efforts to achieve research objectives are carried out using the Systematic Literature Review method with bibliometric analysis or VOSviewer. The purpose of this study is to become a reference for determining research topics on ITS in the field of education, especially the military.

The Impact of Emotion Recognition Models Towards Believability Factor of Chatbots

Emotionally-aware chatbots are chatbots that are equipped with emotional intelligence. Based on the literature, using emotional elements in chatbots can improve user engagement and believability factors. This study attempts to make a novel contribution by empirically evaluating the impact of emotion recognition models on the believability factor of chatbots. This study examines the impact of the emotions model and avatar on chatbot interactions through three implementations. Thirty-one participants volunteered to evaluate emotionally aware chatbots. The participants evaluated the interaction with the chatbot using the Godspeed Questionnaire Series (GQS). The questionnaire results are utilized to measure the effect of the emotions model on the chatbot's believability factor. The evaluation results of the experiment show that implementing the emotion recognition model on the chatbot increases its believability factors. On average, the believability measures in interaction type B (with an emotion model) are enhanced 1.71 times compared to interaction type A (a basic model). Furthermore, the believability measures in interaction type C (with an emotion model and an avatar) are enhanced 2 times more than in interaction type A (a basic model). The believability factor is also heightened by integrating a chatbot avatar into the interaction system. Using avatars in chatbots increases the believability variables of the system by 1.17 times if compared with not using avatars.

Development of Intelligent Tutoring System Model in the Learning System of the Indonesian National Armed Forces Completed with Bibliometric Analysis

This research aims to develop and design an Intelligent Tutoring System (ITS) model to improve the efficiency and adaptability of the learning process using technology based on student personalization. This study employs a systematic literature review method, as well as bibliometric analysis and visualization using the VOSviewer application. We collected data from several well-known article databases. The results showed an intelligent tutoring system model to be connected to five components and 22 sub-components. These components consist of a student model, a pedagogical model, an interface model, a tutor model, and a domain model. Thus, these components and sub-components produce a responsive student model that can be used to develop a learning system within the Indonesian National Armed Forces. The responsive student model not only assesses students' visual, auditory, or kinesthetic learning styles but also gauges their emotions or concentration levels during the learning process. This development not only increases learning effectiveness but also improves student learning experiences and supports adaptive and sustainable academic growth.

Measuring Anticipated User Experiences for Enhancing Development, Case Study on E-commerce

This study analyzes how user experience plays a vital role in the software development and management decision. In this paper, the user experiences are focus on e-commerce as case study. The perception and understanding of potential users are gathered through a survey test. Survey as one of the tools to measure user experiences will be analyzed and used to improve the design of a website, in this case, an e-commerce site. The result will be used for management and developer to plan the next plan. This study shows measuring user experiences can be done as soon as possible as part of development, as anticipation. The result of experiment shows the different expectation between man and women. The success in launching an application will be depend on how the user expectation in using the application. Furthermore, research on the benefit of user experience measurement can explore more in depth.

Smartphone Application Using Fintech in Jakarta Transportation for Shopping in the Marketplace

Mobile Application for Elderly Care

This paper discusses the design and implementation process of mobile applications used by nurses to communicate with the elderly or with people appointed to represent the elderly in using this mobile application. This mobile application is expected to help caregivers or nurses monitor the health condition of the elderly, and this mobile application has several functions that make it complete. These functions include submitting weekly reports, communicating with others via forums, and providing relevant information about the elderly and how to deal with them, including news and government regulations on elderly care. This mobile application uses case diagrams to describe business processes and class diagrams to illustrate database design. As for the implementation, android studio and MySQL database are used for database storage. This paper is limited to 9 references for literature review previous similar research by searching for publication titles with the sentences “mobile application”+elderly+care using quotation marks and without quotation marks for the words elderly and care for all years of publication.

Finding Features of Multiple Linear Regression On Currency Exchange Pairs

Due to the prospects for financial gain, forex is always attractive to many people. However, because forex market analysis is not simple, a computer is needed to assist in creating predictions using features that are understandable to people. This study employs the Multilinear Regression technique to identify these kinds of features. The features and prediction target have a very strong correlation. With a very low RMSE and a very high R square, the prediction quality is quite outstanding. The outcome will help academics in the forex field use machine learning algorithms to make better predictions.

Method to Profiling the Characteristics of Indonesian Dangdut Songs, Using K-Means Clustering and Features Fusion

There have been numerous studies that discuss profiling for various subjects, including criminal profiling, consumer profiling, and employee profiling, among others.However, song profiling is a relatively rare and underexplored area.In fact, profiling songs can provide us with new insights.Dangdut, one of the most popular musical genres in Indonesia, is a unique blend of musical rhythms from Arabic, Malay, Indian, and local music, and has the ability to captivate listeners and get them dancing and swaying along.In this study, we utilized feature selection techniques and feature fusion in conjunction with the K-Means clustering method to profile 281 Dangdut songs into two groups of clusters, with the best Silhouette score of 0.646.Additionally, we compared our method with non-Dangdut song data and obtained a Silhouette score of 0.549.

Improving the quality of enterprise IT goals using COBIT 5 prioritization approach

Utilization of Information Technology is increasingly high at this time requires the guarantee of any information processed by the system on a company. One of the benchmarks used is to measure the level of maturity of IT Governance. COBIT 5 becomes the main reference that is often used for these needs. But to do the assessment takes a long time because of the many processes assessed. This becomes an obstacle for a very dynamic company, the business has changed but the new assessment is completed. The authors propose a new approach using a priority model that adopts a enterprise strategy and incorporates it with a follow-up of previous IT Governance audit results. Tests have been conducted one of the state-owned enterprises in Indonesia engaged in the financial sector and has a dynamic business. The results showed that the assessment can effectively improve the quality of corporate IT objectives with the acceleration of the assessment process up to 20%.

The Form of High-Performance Computing: A Survey

Abstract Computational technology is an important thing that needed in the whole of human life. Along with the development of computer technology, there are a significant data created and wait to be processed; we will get more advantages if it well-processed, if not we will see the data explosion, the data is just a useless stack. Research mitigates the other research paper and makes a conclusion and a report by PRISMA method. The development of computer technology will support the development of an organization. Considering any factors in choosing the right and suitable high-performance computer resource due to our organization is a must. The existing high-performance computing forms are Supercomputer, Grid Computing, Cluster Computing, and Cloud Computing. Based on this work, the last form of high-performance computing, Cloud Computing is one of the most used forms thus we mitigate its advantages and disadvantages. Recently, to process this vast data, we must have a high-performance computer and to provide it, this is not a cheap resource.

Visual learning as Object Recognition to Recognize Image for Mental Disorder Children

Technology has improved a lot comparing to the previous years. The improvement can be seen from various development of a program that may benefit human life. One example of the advancement of technology is artificial intelligence (AI). AI has taken a considerable part in becoming the primary necessity in computer programming. One famous example of AI is object recognition. An arrangement of codes is established for the machine to recognize a picture that is inputted. Even so, the use of object recognition programs that may help mental disorder children is still low -- considering that they are still in the age where education is crucial. However, the standard approach of teaching them is not the right way to make them understand since they need individual adjustments and appropriate training for them to follow. Therefore. We are proposing an application that will help to educate mental disorder children through the use of object recognition in visual learning. The app will have built-in features such as vocalize the object's name, which will help mental disorder children to know and remember how it is pronounced. Other features to further improve the education has guessed the word and draw the object which will help them to understand how the object is shaped. Moreover, Researches have proved that learning visually will help mental disorder children to understand better.

Model Technology Service E-Participation - Voting (E-PV) For Political Communication Between The Council Of Regional Representatives (DPRD) And The Citizens Using The Framework Information Technology Infrastructure Library (ITIL V.3) Transition Domain Service

E-voting is an electronic voting information technology service, while E-participation is an information technology service for citizens of the public to provide reports, polls, or suggestions on government policies electronically. The Technology Services E-Participation-VOT (EPV) model is a combined E-Participation and E-Voting service model. The concept of this service model is that citizens transform from E-participation and E-voting services into an EPV model. The purpose of this model is to provide information to participants (citizens) about online-based voting services. This method of developing the EP-Voting Anticipation (EPV) technology model uses agile methods that are effective and flexible in various cases of designing Information Technology models for Information Technology (IT) services that apply a Framework Information Technology Infrastructure Library (ITIL). The results obtained from this study are an Information Technology service system model that provides recommendations for participation in conducting electronic elections (E-Voting).

Prediksi Prioritas Infrastruktur Jalan di Provinsi Banten Dengan Metode AHP

Jalan merupakan sarana infrastruktur utama yang sangat dibutuhkan dalam mendukung kelancaran transportasi guna meningkatkan kondisi perekonomian masyarakat. Seiring dengan berjalannya waktu jalan akan mengalami kerusakan baik karena faktor alam maupun karena faktor teknis lainnya, sehingga diperlukan upaya penanganan serius dari pemerintah setempat. Dalam hal penanganan kerusakan jalan dibeberapa wilayah masih ditemukannya permasalahan yang perlu segera diselesaikan, salah satunya adalah permasalahan ketersediaan anggaran yang tidak sebanding dengan tingkat kerusakan jalan yang cukup banyak sehingga diperlukan sebuah metode untuk mengetahui urutan prioritas jalan harus segera ditangani. Untuk menentukan prediksi dan urutan prioritas penanganan jalan maka digunakan metode Analytic Hierarchy Process (AHP) dengan pertimbangan faktor kerusakan, lalulintas dan ekonomi.

Mobile application for the futsal sports community in Jakarta

The rapid development of technology makes many changes that occur in the community in carrying out daily activities, one of which is the use of online-based applications to support community activities in Jakarta. This study aims to build applications to support community activities in the field of futsal sport, namely ordering Android-based sports arenas. This ordering application consists of Home, Schedule, sparring, profile, info, and ordering. This application makes it easier for people to find information about the sports arena and make it easier to order sports fields anytime, anywhere, so they can increase profits for the sports arena business. This mobile application was applied with android studio and using MySQL for the database. Hopefully, creating this mobile application will reduce the number the crime rate committed by youth and expand their friendship not only around their house or school but for future development it is possible to run a competition that can be held at the provincial level, or at the national level and possibly even at the South East Nation country (ASEAN) level which involving several ASEAN countries.

Learning the Jawa Culture by using Android Smartphone

Javanese culture is a culture that the Javanese own, and the Javanese have started to exist with their culture since before the era of the Majapahit kingdom, and the Javanese are famous for their ability to sail the oceans. In addition, the many temples built, which are currently historical sites, prove that the Javanese are a large ethnic group that inhabits the island of Java. Other Javanese cultures include Javanese handicrafts such as kris, Javanese dance, Javanese batik art, Javanese wayang art, Javanese carving art, Javanese songs, and Javanese writings. All these cultures are part of the wealth of the unitary state of the Republic of Indonesia that must be preserved and passed down from one generation to another, and current technological developments tend to make the current generation less aware of preserving the culture of their ancestors. Therefore, we use technology to create an application for learning Javanese culture by applying game elements as entertainment elements. The business process of this application is modeled using use case diagrams and class diagrams to describe the database relationships. This mobile application is built using android studio and uses a MySQL database.

Blockchain Technology Factor for Improve Good Distribution Practice in the Pharmaceutical Industry

The distribution process is one of the processes that occur in all industries, including the pharmaceutical industry. The distribution of medicines in the pharmaceutical industry is the most important to provide quality pharmaceutical products to patients or consumers. With several problems that occur in the distribution of drugs in things that currently should be considered. This happens a lot due to the many drugs that disagree with the provisions or such illegal or false drugs. This research investigates the use of blockchain technology for drug distribution in Indonesia. This research uses a systematic literature review method with related literature. The results of this research found 54 blockchain factors that were used to improve nine aspects of good distribution practices and to ensure the good quality of medicines distributed to consumers.

Decision Support System for the Selection of the Best Political Figure through social media using the Analytical Hierarchy Process (AHP) Method

Political communication can be interpreted as a way of influencing power, policy, ideology with the aim of establishing a dialogue between political actors and citizens. Social media can be used to build more effective political communication. In political campaigns social media can also influence community groups. The problem that occurs today is that political figures often create political content on social media to seek political support. But his supporters find it difficult to determine which political figures he is popular with and interested in. This research provides solutions to provide an overview of the selection of the best political figures. The purpose of this study is to determine the criteria for selecting the best political figures in Indonesia in the decision support system. The method used in this decision support system is the AHP (Analytical Hierarchy Process) method. The result of this study is the application of a decision support system to help political supporters determine the best political actors based on the criteria and weight of each political actor.

A Literature Review of Music in Computer Science

Music has been a big part of human life since ancient civilizations, and listening to music is fun and for the benefit of humans.Over time, computer science continues to develop, and music begins to be recorded digitally and belongs to the group of unstructured data whose data needs to be managed to become structured data.Much academic work has analyzed how music is digitized, and there is also mixed analysis of the negative effects of music, but most research agrees that digitization has many positive effects in the computer science-backed music world.This paper analyzes and reviews papers that discuss the application of computer science technology that plays a role in managing digital music data, which is better known in the form of sound or sound.Many papers analyze what people think about the use of technology in music education, and several have proposed different ideas that can be applied.The literature review discussed in this paper is divided into three parts: the rise of digital music, how computer science can be integrated into music education, and music creation through computer science.The literature review in this paper is carried out in 4 steps: paper collection, selective screening, classification, and summary analysis.

Smartphone Application for the Deaf and the Deaf Caring Community

In life, various challenges and problems faced by deaf people such as communication skills and other problems, including emotional, mental, and societal development However, technology is needed that can help the process. This Smart Application try to build communication between the deaf to share thoughts in group chats, including getting information about the deaf, so that the communication learning process for the deaf becomes easier. The proposed model is using Unified Modeling Languages (UML) diagrams, such as case diagrams for the current proposed idea process and class diagrams for relational tables or database stores. In addition, the User interface (UI) is implemented in this research work, Personal Home Pages (PHP) as a web server programming and MySQL database as an open-source database.

Dynamic Programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

Indonesian Twitter Emotion Recognition Model using Feature Engineering

Twitter is a social media platform that has a large amount of unstructured natural language text. The content of Twitter can be utilized to capture human behavior via emphasized emotions located in tweets. In their tweets, people commonly express emotions to show their feelings. Hence, it is crucial to recognize the text’s underlined emotions to understand the message’s meaning. Feature engineering is the process of improving raw data into often overlooked features. This research explores feature engineering techniques to find the best features for building an emotion recognition model on the Indonesian Twitter dataset. Two different text data representations were used, namely, TF-IDF and word embedding. This research proposed 12 feature engineering configurations in TF-IDF by combining data stemming, data augmentation, and machine learning classifiers. Moreover, this research proposed 27 feature engineering configurations in word embedding by combining three-word embedding models, three pooling techniques, and three machine-learning classifiers. In total, there are 39 feature engineering combinations. The configuration with the best F1 score is TF-IDF with logistic regression, stemmed dataset, and augmented dataset. The model achieved 65.27% accuracy and 66.09% F1 score. The detailed characteristics from the top seven models in TF-IDF also follow the same feature engineering configuration. Lastly, this work improves performance from the previous research by 1.44% and 2.01% on the word2vec and fastText approaches, respectively.

Suitable Deep Learning Based for High Accuracy Object Detection in Inventory Management: Systematic Literature Review

In the competitive retail industry, maintaining optimal inventory levels is crucial for ensuring smooth business operations. Accurate inventory management goes beyond a mere recording task. The integration of information technology, especially machine learning for inventory management, has become indispensable for optimizing inventory and achieving cost savings. Object detection techniques, have potential in precisely identifying objects and stock-out. However, so many options for object detection models makes the research process more extensive when determining the appropriate model. This study aims to assist researchers in determining the best object detection model to use in the development of current deep learning-based systems, without the need for extensive preliminary research on all available deep learning models using systematic literature review approach. From the research, it is found that the RetinaNet, YOLO, Faster R-CNN, and Mask RCNN are the most suitable choices for inventory management studies, and could reach high accuracy rate above ${9 7 \%}$.

LC-MS Analysis: Mini Review Frequently Used Open Source Softwares

This paper provides information about open source softwares that most often used as a tool to analyze data generated from the Liquid chromatography-mass spectrometer (LC-MS) instrument and including a little discussion about how LC-MS works. LC-MS consists of Liquid Chromatography and Mass Spectrometer analytical instruments. This device extensively used in Metabolomics, because it provides more comprehensive information about the metabolites. It also shows the breadth of the diversity of chemical compounds in metabolites that make difficult and time-consuming to identification of metabolite's structures. This is an obstacle in efficient and accurate identification. So, many open source softwares developed to simplify and speed up the analysis and interpretation of LC-MS result. There are popular open source softwares. We compiling mini review of this open source softwares. The conclusion is open source softwares quite helpful in terms of data analysis and interpretation of compounds contained, but no one has provided a single interpretation, still need experts for reliable interpretation.

DSS to Determine the Location of Housing Using the Fuzzy Method

The choice of location to optimize business activities such as housing, factories, shop houses is determined through mechanisms and patterns that can be understood. Various criteria that have been taken into consideration include land availability, raw materials, distance, accessibility, transportation, labor costs, security guarantees, absorption of local markets, political stability, and other supporting facilities. Some location theories generally use the approach of minimizing costs, maximizing profits, market approaches, attractiveness. One effort to help facilitate housing developers in optimizing it is to build software (software) based on fuzzy artificial intelligence applications. Based on the decision-making criteria determined by the entrepreneur. So entrepreneurs, especially in the field of Developer / developers no longer bother to find out or get information about the location of housing construction desired by consumers, and can reduce unwanted risks and time savings.

SOFTWARE TESTING BY USING THE BLACK-BOX METHOD AND THE EQUIVALENCE PARTITION TECHNIQUE TO PREDICT THE ACCURACY OF THE NEURAL NETWORK BASE

A neural network algorithm is an artificial nervous system or artificial neural network, it is a physical cellular system that can acquire, store and use the knowledge gained from experience for activation using bipolar sigmoid where the output value ranges from -1 to 1. Because there is a yet of a neural network algorithm model to predict the level of accuracy in terms of software testing, the equivalent partitions black-box technique is used. The black-box software testing method is a testing approach where the data comes from defined functional requirements regardless of the final program structure, and the technique used is equivalent partitions. The design of the accurate prediction of this research is by determining the college application as the software to be tested, then testing it using the black-box method with the equivalence partitions technique. This test was chosen because it will find software errors in several categories. From black-box testing, a dataset is obtained to measure the accuracy of real output and predictive output. The last step is to calculate the error, RSME from the real output, and the predicted output. Furthermore, the final results of the research on the neural network algorithm that is applied to determine the prediction of the accuracy level of black-box software testing with the equivalent partitioning technique is the average accuracy above 80%.

Mining Frequent Pattern on Liquid Chromatography-Mass Spectrometer Data of Rodent Tuber to Find the Association Rules of Compounds for Machine Learning

Algorithm Model Determination of DNA Primer Design For The Success PCR Process

Deoxyribonucleic Acid Primer Design is an important factor in the success of the Polymerase Chain Reaction process because it will determine the value of Guanine and Cytosine content and Melting temperature for the genome amplification process in the adequacy of research data. To get a good primer design, a simulation process is needed to get the desired results, namely Guanine and Cytosine content between 50%-60%, Melting temperature 50o -65oC, a minimum length of 70 base pair sequences and the temperature difference between the forward primer and reverse primer less than 5oC. Usually researchers use the Primer3plus or NCBI Primer-BLAST software to get the Deoxyribonucleic Acid Primer Design results. Because there are limitations in the use of the software, the researchers, researchers want to use software that are suitable for current and future research needs. For this reason, a new algorithm model is needed to make software to support the needs of researchers. The method used in the algorithm model is to use Start Codon ATG and Stop Codon TAA, TAG and TGA with sequence lengths of 18, 21 and 24 base pairs. The raw data used is the Deoxyribonucleic Acid of the Typhonium Flagelliforme plant which has potential anti-cancer compounds. The new algorithm model resulted in 22 (twenty two) Deoxyribonucleic Acid Primer Design options, all of which met the data constraints and research requirements.

Regional Classification of Indonesian Folk Songs Based on K-NN and PCC-LDA Model

One of the biggest challenges in Music Information Retrieval (MIR) is classification. MIR is a subset of Machine Learning (ML), where the methods found in computer science can be used to solve problems in the music domain area. Indonesia is an archipelago country inhabited by around 1,340 ethnic groups spread across 38 provinces. This ethnic group has a variety of cultures, including folk songs, and almost every area in Indonesia has folk songs. In this study, we conducted a regional classification of 500 Indonesian folk songs from 10 regions in Indonesia. We build a model for regional classification tasks using the MIR approach. This model combines feature selection and dimension reduction capabilities. By using hyperparameter tunning and a cross- validation of 5 folds, the result of our proposed model for regional classification of Indonesian folk songs using K-NN with PCC-LDA can obtain an accuracy rate of 84.7%.

An Interactive Sign Language Based Mobile Application for Deaf People

Disability is a complex problem affecting many lives in Indonesia, especially people with hearing impairments. Disability is also a complex phenomenon, starting from the type of disability, degree of disability, and age of persons with disabilities to interactions and environmental impacts on disability. Moreover, there are many testimonies from around the world that people with disabilities are more likely to be weak and less likely to get an education. This paper aims to present a mobile application that offers sign language images and videos of any text in a learning environment that is shaped like a dictionary module to increase their vocabulary, learn a new sign language, and pronounce a word. This mobile application is specially designed for deaf people who have limitations in learning and educational training. This mobile application is equipped with several features such as user registration for the deaf and normal people who like to learn sign language, discussing through forums, using a sign language dictionary containing sign language text and pictures, using interactive learning videos, registering for an event activity, including giving input, and also make a donation.

The Country's Implementation and Adoption of Standardized Health Terminologies to Promote Interoperability: A Systematic Literature Review

Modern healthcare systems demand comprehensive information systems but face obstacles during adoption. Organizational and structural complexity, especially decentralized systems, challenges the integrated management and sharing of data across information systems. Interoperability is critical to address these issues effectively in heterogeneous and distributed environments. However, different medical terminology hinders interoperability within health information systems. To address this, a systematic literature review examined various medical terminology standards for national interoperability. From 790 articles, 12 were selected according to PRISMA guidelines with inclusion and exclusion criteria. Standard health terminology is critical to achieving interoperability, and many countries have adopted it, although some have required adaptation to accommodate local contexts. Data models, governance, and maintenance of standards are equally important in achieving national interoperability. In addition, standardized health terminology promotes consistency and uniformity in terminology, which is beneficial for decision-making by stakeholders in the health system.

Bibliometric Analysis using Vos Viewer with Publish or Perish of Intelligent Tutoring System in Private Universities

The objective of this study is to analyze the development of intelligent tutoring systems in private universities. We conducted the analysis using bibliometric methods, utilizing the Publish or Perish and VOSviewer applications. Data was obtained by using the publish or perish application with the keyword "intelligent tutoring system in private university" from the Google Scholar database from 2019 to 2024. According to search results, the number of research papers has decreased from 117 to 23 from 2020 to 2024. Mapping using VOSviewer application produces three types of visualization, namely network, overlay, and density visualization. In its conclusion, this research notes a decrease in the number of studies discussing in private universities since 2020, but still shows great potential for development by other researchers.

Memetic Algorithm Small Survey For 2019 Published Papers

The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary Algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides a comprehensive survey of MA research published in 2019, reviewing 75 selected papers from an initial pool of 112 identified through Google Scholar. The selected papers were categorized into five types: optimization problems (40 papers), image processing (10 papers), parallel processing (5 papers), gene/DNA datasets (4 papers), and other applications (16 papers). The survey highlights MA’s versatility and effectiveness across various domains, particularly its potential for solving complex optimization problems. Key findings include the adaptability of MA for diverse applications, its ongoing relevance in addressing challenging issues, and promising opportunities for combining MA with other algorithms to enhance performance. The paper also emphasizes the significance of MA in fields such as image processing, where it improves pattern recognition and image enhancement, and in bioinformatics, where it optimizes gene selection and genetic algorithms. Despite the extensive study of MA, there remains a significant research gap in non-English literature, particularly in Bahasa, limiting accessibility for Indonesian researchers. This survey aims to bridge this gap by providing valuable insights and encouraging further exploration and application of MA to solve increasingly complex problems. It offers a comprehensive overview that underscores the importance of MA and its potential for future research and innovation.

Leveraging Social Learning for Improved Cybersecurity Maturity: A Case Study Using the NIST Framework

This research shows that social learning can be used to increase an organization's cybersecurity maturity level. Using a literature study and case study approach. Literature studies are used to identify social learning's key success factors (KSF) and challenges in general. Then, the case study was performed in the context of assessing the maturity level of information security using the NIST Cybersecurity framework at ABC Software House, a small-scale software development company in Indonesia. Assessment activities are performed through FGDs which then also identify the most relevant social learning’ KSF to increase the organization's information security maturity level. Next, referring to the selected KSF, a process of identifying and mapping social learning’ challenges was carried out. The integration of social learning can help organizations gain better focus and understanding in nontechnical contexts to increase their maturity level. This research highlights that integrating social learning with the NIST cybersecurity framework can provide organizations with better focus and perspective for formulating non-technical solutions. This study also enriches the existing literature on social learning in cybersecurity and offers practical insights for organizations looking to improve their cybersecurity resilience.

Adoption of Blockchain Technology for ASCM Solution: A Systematic Literature Review

With the global population projected to exceed 10.9 billion by 2100, agriculture faces significant strain. One approach to alleviate this pressure is through the use of blockchain technology, which could improve the traceability and transparency of agricultural products. This study investigates blockchain’s characteristics, advantages, and challenges to determine its suitability for agricultural supply chain management (ASCM). The methodology involved selecting keywords and conducting searches for papers published between 2017-2021 across multiple scholarly databases including Google Scholar, Scopus, Cross Ref, Science Direct, and Emerald Insight. The PRISMA method was employed for the literature review, resulting in the analysis of 91 papers. The analysis identified the top ten most commonly discussed blockchain characteristics. Findings suggest that blockchain technology offers advantages such as increased operational efficiency, enhanced management data transparency, intelligent contract management, and mitigation of fraud, errors, and financial losses in ASCM. However, blockchain adoption faces challenges including regulatory hurdles, stakeholder relationships, data ownership, scalability issues, and knowledge gaps. This study contributes to the understanding of blockchain's potential in ASCM and underscores the importance of addressing these challenges for its effective implementation.

Designing Knowledge Management model for curriculum development process: A case study in Bina Nusantara University

Curriculum is a core process in higher education institution. The curriculum development is a complex process and needs internal or and external data, information and knowledge as well. This study proposed a Knowledge Management model for curriculum development process. The survey was conducted in Bina Nusantara University and interview the staff who were involved in the process of curriculum development to understand the process. Literature studies were applied to develop a model that fit on integration between knowledge management systems as knowledge sources into Learning Management Systems at Bina Nusantara University as case studies. As results, this Knowledge Management model support several stakeholders like Study program, Subject Content Coordinator, Lecturer, Student, and Alumni & Partners to improving existing curriculum development process.

Adaptive Game Design using Case-based Reasoning Method for High Performance Computing Learning

Computer games have become an entertainment tool for all ages and genders. The popularity of the game has raised the attention of many researchers from various research domains. But there are some problems in game design. One of these problems is a game adaptation that can be defined freely as the game's ability to adapt to various factors such as: gamer's skills, emotion playing, layer formats, and hardware platforms. The purpose of this paper is to propose a case-based reasoning (CBR)-based game architecture for designing adaptive text-based computer games that will be used as a delivery of high performance computing (HPC) learning.

Learning decision rules from incomplete biochemical risk factor indicators to predict cardiovascular risk level for adult patients

This study aims to learn decision rules from input dataset using decision tree learning as a supervised classification method to predict cardiovascular risk level for adult patients (above 30-year-old). Dataset for this study is provided by a blood chemical lab from a private hospital in Southern Jakarta and used under permission. The experiment results using CART algorithm found an optimum decision tree to represent decision rule made previously by a Physician in predicting Cardiovascular risk level. The with a 9 tree-depth of 13 features that achieved 97.3 % training accuracy and 96.8 % testing accuracy respectively. Further decision tree simplification discovers a set of rules to predict level of Cardiovascular risk despite incomplete predictors as input.

Use case point as software size measurement with study case of Academic Information System

Academic Information System at Satya Negara Indonesia university (USNI) was developed since 2008 and as major systems required for the processes of academic activities process, which support the teaching learning process activities. The Academic information system serving students carry out lectures and gain value in the form of cards in each semester of study results. In order to rebuilding this system the current system will be measured with Use Case Point (UCP) in order to help management when extend the current Academic Information System. UCP will help the management when making their decisions regarding with the development of system in term of time, people and money. Measurement software using the UCP in Academic Information Systems of Satya Negara Indonesia university has score Use Case Point (UCP) = 86.864 and categorized as small software size project which is smaller than 99.

Delivering an interactive presentation in supporting of dynamic teaching method with an IT Blueprint framework: IT Initiative-ITBluTric

Education and technology have a very close relationship and it cannot be denied. Education is now very depending with technology and always keeps evolving to support each other. Teaching methods are now drastically change because the present of rapid development of technology. The way of teaching is now shifting due to the available technology and the students, parents and the society no longer accept the conventional teaching method, which is one-way communication. Presenting any materials with the help of technology, increase the successful rate of delivery of the materials and also increase the student's understanding. IT Initiative in IT Blueprint framework is one of the method to reduce the failure of the IT project (developing interactive presentation) but it still lack to measure the IT Initiative. The new measurement method in IT Blueprint called ITBluTric is needed to become a success factor while developing the interactive presentation.

High Performance Computing (HPC) Implementation: A Survey

To maintain excellence in global hyper-competitive economies in the upcoming decades, manufacturers must improve the design, development, and distribution of subsequent product generations, production technologies, equipment, and processes. High-performance computing (HPC) solves the application of significant challenges using computer modeling, simulation, and analytical technology. By using HPC technology, products design will be faster, making and testing prototypes will save time, the production process can be simplified, the cost of innovation will decrease, and high-value innovation will be quickly developed. This study uses the PRISMA method, based on predetermined keywords and analyzes the algorithms, frameworks and programming languages and the processes that are most commonly used to optimize the Graphics Processing Unit (GPU). The results of this study can be a reference for future research on the implementation of High-Performance Computing (HPC) Application by combine modeling, simulation, and analysis.

Cloud Computing Adoption Strategy Planning at Agricultural Central Data and Information System, Ministry of Agriculture with Roadmap for Cloud Computing Adoption (ROCCA) Model

The goal of this study is to help Center for Agricultural Data and Information Systems (Pusdatin), Ministry of Agriculture, Republic of Indonesia, on the development of a strategy for private cloud computing adoption. Pusdatin sees cloud computing as a technological innovation opportunity that promises lower costs, provides flexibility and optimization of resource, and ease of monitoring and management of servers and applications that can become a solution for the problems and concerns faced today. However, Pusdatin is faced with questions of how to properly adopt cloud technologies in their environment and gain all those benefits. This study uses Roadmap for Cloud Computing Adoption (ROCCA) model for the adoption strategy development. ROCCA adoption model consists of 5 phases namely the analysis, planning, adoption, migration, and management. Data were gathered from an interview, direct observation, and running several discovery tools within Pusdatin's IT environment. Data then processed through each phase of ROCCA model that produces the analysis, establishment of cloud program team, criteria, cloud architecture, migration steps, timeline, and priority that forms the detailed strategy for adopting cloud computing. The results of this study are recommended strategic steps to simplify and accelerate the implementation of cloud computing technology at Pusdatin.

Strategic Planning Of Information Systems And Information Technology At Agricultural Research And Development Agency, Ministry Of Agriculture

The research was conducted at the Agency for Agricultural Research and Development (IAARD), Ministry of Agriculture. The purpose of this research is to do the strategic planning of IS / IT Balitbangtan to support Balitbangtan vision and mission, to align business strategy and IS / IT strategy, formulate IS / IT business strategy, IS / IT management strategy, and IT Strategy. This research uses qualitative approach using Ward & Peppard method. The result is five strategies that can be input for future Balitbangtan development. The five strategies are: (1) Utilizing the advantages of technological innovation to improve the competitiveness of Balitbangtan; (2) Utilizing the benefits of technological innovation to continuously manage-update database of agricultural research results;. Next are, (3) Increasing the number and skills of human resources to build a database on agricultural research results; (4) Exploring the utilization of VPNs to develop science and technology facilities from upstream to downstream; (5) Building infrastructure to improve e-office programs across the UK / UPT. There are finding sixteen IS/IT strategic planning for IAARD which should be applied to make proper governance process.

Comparing CART and C5.0 Algorithm Performance of Human Development Index

Human Development Index (HDI) is a reference to how far the administration can develops their people. The development of a territory is related to a prosperity of the people. This research is focused on Human Development Index in West Java, a Province of Indonesia. Research identifies variables which are influence and very important to HDI and compares the model of CART and C5.0 algorithm based on HDI concept that has developed by United Nation (UN). Research uses 14 variables which are commonly used for measuring HDI and found that there are two variables that most influencing to HDI, MYS-index and EDU-Index. By knowing the MYS and EDU-Index research can predict and classify each territory to its HDI. Research could be conducted to predict HDI in the other territory. The results evaluated by measuring the accuracy and confusion matrix, 91.35 for CART algorithm and 90.80 for C5.0 algorithm.

Mining Association rule with Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) data mining technique

Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) as proposed new data mining technique has been success to mine frequent and similar patterns. Moreover, the AOI-HEP data mining technique has been improved with confidence mining pattern into two terms, where firstly give confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern. Secondly, give confidence for each dataset as confidence AOI-HEP pattern between frequent and similar patterns. Confidence for each AOI-HEP pattern will show how interested each of AOI-HEP pattern between frequent and similar pattern, whilst confidence per dataset will show how interested each dataset between frequent and similar pattern. In this paper the mining of AOI-HEP has been extended to mine AOI-HEP association rules using the current AOI-HEP frequent pattern, since association rule deal with frequent pattern. Based from previous experiments upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS, there are 9 finding AOI-HEP frequent patterns where 8 from adult and breast cancer datasets respectively. Finally, there are 2 and 1 AOI-HEP association rules from adult and breast cancer datasets respectively.

Big Data implementation for Inventory warehouse systems

Inventory Warehouse Systems as the back-office application for dealing with a supplier is an essential application for supporting front office application which coping with daily customer transactions. To increase the inventory warehouse system performance, then business process as Online Transactional Processing (OLTP) should be separated from the reporting process as Online Analytical Processing (OLAP) application. Big Data as the current most alluring technology, is undoubtedly the top choice in intelligent application implementation. To understand what is “Big Data,” then we should refer to previous technology and Big Data as a metamorphosis from last OLAP technology such as Data Warehouse, DSS, EIS, BI and so on. Thus, since Big Data as a metamorphosis from previous technology then we can apply Big Data for OLAP process to create an intelligent application which supports reporting for the decision-making process. This Big Data implementation was running on one single node/computer, where Hadoop for windows x64 was installed. The proposed Big Data application was split into two sub-processes such as Big Data process and Big Data result. Big Data process reads the OLTP database and export into an input text file, and the text file was processed with MapReduce algorithm and import into output text file. Meanwhile, Big Data result shows the summarization of Map and Reduce process from output text file.

Prototype Data Mining Pola Jabatan Fungsional Dosen Menggunakan Teknik Emerging Pattern: Studi Kasus Universitas Mercu Buana

Abstract&#x0D; &#x0D; Lecture Functional position is used to ensure the development of lecturer carrier, structural position, and professionalism improvement. Every lecturer has a right to propose a functional promotion as long as their education background is linear. Data mining has been widely used to analyze data into information in term of patterns that are easily understand by the users. In order to create a functional position pattern that produces beneficial and useful results, the data mining techniques are utilized for analyzing, for example the emerging pattern. The emerging pattern technique represents very strong distinguishing knowledge between datasets and shows an accurate classification ability. The results of data mining with emerging pattern techniques were used in this study to obtain patterns of functional positions and to classify lecturers' educational linearity. The learning data results obtained a confidence value for the sex dataset confidence values for male and female itemset of 50%. Dataset functional lecturers having higher confidence was Lectors of 300 points with a confidence value of 73%. Department dataset which had higher confidence value was psychology by 85%. The highest confidence value in the dataset from tertiary institutions originates from abroad by 62% and for the age dataset that most have functional lecturer positions were aged 51-70 years with a confidence value of 55%.&#x0D; &#x0D; Keywords: Data Mining, Emerging Pattern, Fungsional Position, Lecturer, Linear Education&#x0D; &#x0D; Abstrak&#x0D; &#x0D; Suatu pola untuk menjamin pembinaan karier kepangkatan, jabatan dan peningkatan profesionalisme dosen disebut Jabatan fungsional dosen. Setiap dosen berhak untuk mengajukan kenaikan jabatan fungsional dengan syarat salah satunya adalah liniear dalam bidang ilmu. Data mining mampu menganalisis data menjadi informasi berupa pola yang mempunyai arti bagi pendukung keputusan. Agar proses dalam pencarian pola jabatan fungsional ini menghasilkan nilai tambah dan berguna maka dibutuhkan teknik data mining untuk menganalisanya. Salah satu teknik dalam data mining adalah emerging pattern. Teknik emerging pattern merepresentasikan pengetahuan pembeda yang sangat kuat antara dataset dan menunjukkan kemampuan klasifikasi yang akurat. Hasil dari data mining dengan teknik emerging pattern adalah mendapatkan pola (pattern) dalam jabatan fungsional dan mengelompokkan kelinieritasan pendidikan dosen. Dari hasil learning data diperoleh nilai confidence untuk dataset jenis kelamin nilai confidence untuk itemset Laki-laki dan perempuan sebesar 50%. Dataset jabatan fungsional dosen yang nilai confidence nya lebih besar adalah Lektor 300 dengan nilai confidence sebesar 73%. Dataset jurusan yang mempunyai nilai confidence terbesar adalah psikologi sebesar 85%. Nilai confidencen yang paling besar pada dataset asal perguruan tinggi berasal dari luar negeri sebesar 62% dan pada dataset usia yang paling banyak memiliki jabatan fungisonal dosen berada pada usia 51-70 tahun dengan nilai confidence sebesar 55%.&#x0D; &#x0D; Kata Kunci: Data Mining, Emerging Pattern, Jabatan Fungsional, Dosen, Linier.