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Journal of Information Systems and Technology Research
Published by Ali Institute of Research and Publication
ISSN : 28283864     EISSN : 28282973     DOI : https://doi.org/10.55537/jistr
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISTR is a periodical journal that aims to provide scientific literature, especially applied research studies in information systems (IS) / information technology (IT), and an overview of the development of theories, methods, and applied sciences related to these subjects Focus and Scope Artificial intelligence Autonomous reasoning Bio-inspired algorithms Bio-informatics Cloud computing Data science Data mining Data visualization Decision support systems Deep learning Evolutionary computation Fuzzy logic Human-Computer Interaction Hybrid intelligent systems, Adaptation and Learning Systems IoT and smart environments Knowledge mining Machine learning Neural networks Pattern recognition Soft computing Prediction systems Signal and image processing System modeling and optimization Time series prediction Web intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 74 Documents
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Development of a Church Asset Management System with an Agile Approach in the Pasundan Christian Church Yulisa Geni, Bias; Edwar Christoper Pendjol
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1173

Abstract

The Manual asset management still used at the Pasundan Christian Church Kampung Sawah Congregation has led to various challenges, including recording errors, data duplication, and limited capabilities for real-time reporting and monitoring. This study aims to design and develop a web-based asset management information system to enhance accuracy, efficiency, and transparency in church asset administration. The system development process adopts the Agile methodology with a Scrum approach, enabling iterative and collaborative development between the development team and stakeholders. The Scrum method includes stages such as User Story, Product Backlog, Sprint, Sprint Backlog, and Daily Scrum. The system is web-based, developed using PHP as the programming language and MySQL as the database. The framework used is PHP. The system comprises 14 core features, including asset recording, modification, deletion, search, printing, and management of asset borrowing and returning. Three types of users are identified: the Household Commission as the primary administrator, the Church Council as borrowers, and the Pastor as a passive user. Testing was conducted using the black-box method to evaluate the performance of each feature. The results show that all 14 features (100%) functioned as expected. Overall, the system successfully improves reliability and transparency in asset management and supports a more organized and efficient organizational service.
OPTICS-Based Clustering of East Java Regencies and Cities by Divorce Factors Nurhalizah, Cesaria Deby; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1227

Abstract

Divorce is a social phenomenon that occurs when a married couple decides to legally end their marriage. This decision is influenced by various factors such as conflict, economic pressure, domestic violence, and deviant behavior. The aim of this study is to group regencies and cities in East Java Province that share similarities in the main causes of divorce, in order to understand the patterns that emerge across regions. The OPTICS (Ordering Points to Identify the Clustering Structure) clustering method was chosen for its ability to identify cluster structures with varying densities. The modeling process was conducted using a proportion-based approach for each causal factor, with optimal parameters obtained through manual grid search using min_samples = 2, xi = 0.05, and min_cluster_size = 0.1. The analysis identified three main clusters, each dominated by conflict, economic hardship, and deviant behavior, respectively. The quality of the clustering was evaluated using a Silhouette Score of 0.588, indicating reasonably good results. These findings are expected to serve as an initial understanding of divorce causes in East Java and can be used as input for the formulation of more targeted social policies.
Forecasting USD to Rupiah Exchange Rate with the Fuzzy Time Series Singh Approach Santika, Reghina Ajeng; Aviolla Terza Damaliana; Mohammad Idhom
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1238

Abstract

The exchange rate plays a crucial role in determining a country's economic stability, especially for countries like Indonesia that rely heavily on international trade. In recent years, the fluctuations in global currency values have intensified, particularly after the trade war between the United States and China began in 2018. These fluctuations have significantly impacted the exchange rate between the Indonesian Rupiah and the US Dollar, which in turn affects the competitiveness of Indonesian exports, increases the cost of imports, and influences key economic decisions made by investors, importers, and exporters. The problem of this research lies in the challenge of predicting exchange rate movements amidst economic uncertainty and currency volatility.  This study aims to address this problem by forecasting the exchange rate of the Indonesian Rupiah against the US Dollar using the Fuzzy Time Series Singh method. This method is chosen due to its ability to capture complex data patterns with high accuracy and simpler computational requirements. The primary objective of the research is to evaluate the effectiveness and accuracy of the Fuzzy Time Series Singh method in predicting the exchange rate of the Rupiah against the US Dollar. The results of this study show that the forecasting model achieved an accuracy rate with a MAPE value of less than 10%, indicating that the method can provide highly reliable predictions, which can assist economic actors in making better-informed decisions in the face of currency volatility.
Design and Development of Web-Based E-Learning System Using Waterfall for Evaluation Septianto, Rahardian; Hidayatullah, Ari
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1269

Abstract

The learning evaluation process at SMK Assyafi’iyah 02 still encounters fundamental challenges, including the absence of an integrated digital system, reliance on manual distribution of questions and answers, frequent delays in score recapitulation, and limited transparency between teachers and students. These conditions often result in inefficiency, data inaccuracy, and difficulties in monitoring students’ academic progress in real time. The research problem addressed in this study is how to design and implement a web-based e-learning application that is able to overcome these obstacles and support more effective evaluation. Therefore, the purpose of this study is to build an information system that integrates exam management, online submission, automated scoring, manual correction, a nd role-based access, with the goal of improving the speed, accuracy, and transparency of learning evaluations. This study applies the Waterfall development method consisting of requirement analysis, system design, implementation, and testing to ensure a structured and systematic process. The contribution of this research lies in providing a practical digital platform for schools that have not yet fully adopted e-learning systems, while the novelty is reflected in the integration of evaluation, communication, and monitoring features specifically adapted to the learning context of vocational schools. The testing results show that the system developed is able to reduce evaluation time, improve data accuracy, and provide better accessibility for both teachers and students.
Deep Learning-Based Sentiment and Emotion Analysis of Social Media Data to Identify Factors Affecting Healthy Food Choices in Urban Communities Rasyid, Rachmat; Rafli R, Muh; Faisal, Faisal; Suherwin, Suherwin; Asia, Siti Nur; Karimi, Amir
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1288

Abstract

The increasing influence of social media on public perception has made it a powerful driver of dietary behavior in urban communities. Nevertheless, the abundance of unverified health information often obscures individuals’ ability to make informed food choices. This study proposes a deep learning-based framework to analyze sentiment and emotion from social media discourse in order to uncover the key factors affecting healthy food decisions in urban settings. By applying Natural Language Processing (NLP) techniques and advanced deep learning models to a large corpus of user-generated content, the research identifies significant patterns linking emotional expression with food-related decision-making. The results indicate that positive emotions, such as pride and satisfaction, are strongly associated with healthy food promotion, while negative emotions, including frustration, are predominantly tied to affordability, accessibility, and convenience issues. Among these, price and food quality emerge as the most critical determinants shaping consumer preferences. These findings underscore the importance of integrating emotional and socio-economic considerations into public health strategies. Beyond offering empirical insights, this study demonstrates the scalability and effectiveness of deep learning in extracting nuanced perspectives from unstructured social media data, thereby contributing a robust methodological approach for real-time public health monitoring and intervention design.  
Real-Time IoT Integration for Coal Production And Distribution Management Sani , Hendra; Rasyid, Rachmat; Asia, Siti Nur; Syamsuddin, Syamsuddin; Suherwin, Suherwin; Șerban, Răzvan
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1295

Abstract

The coal production and distribution industry faces persistent challenges in data management, operational coordination, and decision-making efficiency. Conventional monitoring methods often result in delayed reporting, low data accuracy, and limited adaptability to dynamic market demands. This study addresses the lack of an intelligent and integrated information system by designing and developing a real-time IoT-based solution for coal production and distribution management. The system was built using the Software Development Life Cycle (SDLC) with the Waterfall model and integrates IoT sensors to automatically capture critical parameters such as pressure, temperature, and coal quality indicators. Artificial Intelligence (AI) components were incorporated to enhance data analysis and support predictive decision-making. System evaluation through simulation with dummy data demonstrated notable improvements, including a 40% reduction in reporting response time and a 95% increase in operational data accuracy. The system also enabled faster production monitoring, streamlined distribution processes, and provided decision-makers with reliable real-time insights. User feedback confirmed the system’s effectiveness in improving accessibility, monitoring efficiency, and overall operational performance in coal production and distribution management.
Comparative Analysis of Deep Learning Models for Wind Speed Prediction Using LSTM, TCN and RBFNN Wardani, Firly Setya; Idhom, Mohammad; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1298

Abstract

Wind speed forecasting plays a vital role in various sectors, including renewable energy management and disaster preparedness for extreme weather events. Accurate prediction models are essential to support decision-making processes, especially in regions with dynamic seasonal patterns. This study compares the performance of three time series prediction models Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Radial Basis Function Neural Network (RBFNN) for forecasting daily wind speed. The dataset consists of historical wind speed data that underwent multiple preprocessing steps, including seasonal-based missing value imputation, stationarity testing, supervised transformation, normalization, and hyperparameter tuning to optimize model performance. The models were evaluated using four standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared (R²), and Mean Absolute Percentage Error (MAPE). The results show that the TCN model outperformed the others, achieving an MAE of 1.117, RMSE of 1.524, R² of 0.120, and MAPE of 20.95%. The LSTM model ranked second with competitive performance, while the RBFNN model produced consistent but slightly lower accuracy. The findings highlight the superiority of TCN in capturing complex sequential and seasonal patterns in wind speed data. The unique contribution of this research lies in integrating seasonal-based preprocessing with a comparative evaluation of three advanced models under varying conditions, including extreme weather scenarios. This study serves as a foundation for developing more accurate and reliable wind speed forecasting systems to support renewable energy planning and enhance disaster risk mitigation strategies.
Intelligent System for Mental Health Disorder Diagnosis Using Certainty Factor Method Sari , Kartika; Winata, Hendri; Riansah, Wahyu
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1308

Abstract

Mental health issues are increasingly recognized as a global challenge, affecting more than 450 million people worldwide, with a significant treatment gap in developing countries such as Indonesia. The limited number of mental health professionals highlights the need for alternative solutions to support early diagnosis. This study aims to design and implement an intelligent expert system for the diagnosis of mental disorders using the Certainty Factor (CF) method. The CF approach was selected for its ability to handle uncertainty and subjectivity in expert reasoning, particularly in cases where symptoms overlap across different disorders. The research methodology includes problem analysis, data collection, system design, implementation, and evaluation. The system was tested using a dataset of mental disorder symptoms, including depression, anxiety, schizophrenia, and bipolar disorder. The results indicate that the system can diagnose bipolar disorder with the highest CF value (0.951), followed by depression (0.883), anxiety disorder (0.853), and schizophrenia (0.510). These findings demonstrate that the CF-based system can provide accurate and realistic initial diagnoses that approximate expert judgment. This research contributes to the field of health informatics by providing a decision-support tool that can be integrated into telehealth platforms, enabling communities to gain faster access to mental health screening.
Functionality, Reliability, and Effectiveness of Information Technology Services in Supporting VAT Administration Hery Setyo Nuryantoro, Petrus; Wahdiat, Irwan Sutirman; Firasati, Aoliyah
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1311

Abstract

This study addresses the persistent challenges faced by taxable entrepreneurs (PKP) in utilizing information technology (IT) for Value-Added Tax (VAT) compliance, including data inconsistencies, reporting delays, and uneven system optimization. The research emphasizes the unique value of integrating IT functionality, infrastructure reliability, and overall effectiveness to improve VAT administration. Data were collected from PKP in the Cirebon 3 region through questionnaires (offline and online) and direct interviews. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to measure the level of IT utilization and its effect on VAT compliance. The f indings reveal that IT functionality, reliability, and effectiveness are at a high level, indicating that IT has been successfully implemented in VAT administration. Input and output tax administration scores were also categorized as high, and regression analysis demonstrated a positive and significant impact of IT on VAT compliance, both partially and simultaneously. These results highlight the critical role of the government’s integrated Coretax system and PKP’s capacity to adopt and optimize IT solutions. The study contributes by showing that combining functionality, reliability, and effectiveness creates a comprehensive framework that enhances compliance and reduces administrative friction in VAT management.
Volunteer Recruitment Information System with Interview Scheduling Using Web-Based Greedy Algorithm Fasya, Muhammad Rezeki; Alda, Muhamad
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1320

Abstract

The rapid advancement of digital technology has significantly influenced the management of non-profit and educational organizations, including volunteer recruitment processes. Gerakan Sumut Mengajar (GSM), a volunteer-based educational movement in North Sumatra, still relies on manual tools such as Google Forms and WhatsApp for participant registration, file verification, and interview scheduling. This manual approach creates inefficiencies, scheduling conflicts, and delays that reduce the professionalism of the recruitment process. The main research problem addressed in this study is the lack of an integrated system capable of handling the multi-stage recruitment process, especially in automating interview scheduling, which is prone to human error and administrative overload. The objective of this study is to design and implement a web-based volunteer recruitment information system equipped with an automatic interview scheduling mechanism using the Greedy Algorithm. The system was developed using the Waterfall software development model, implemented with PHP Native and MySQL. The Greedy Algorithm, particularly the Activity Selection Problem approach, was applied to optimize interview scheduling by allocating available slots efficiently according to the number of interviewers and candidates. The results demonstrate that the system successfully automates registration, document verification, and interview scheduling while minimizing scheduling conflicts. Compared with manual methods, the proposed solution reduces administrative workload, enhances efficiency, and provides a more professional recruitment experience. This study contributes to the development of intelligent scheduling systems in volunteer-based organizations and highlights the applicability of heuristic algorithms in solving real-world scheduling problems.

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