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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 104 Documents
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Ethical Implications of Artificial Intelligence in Lifelong Learning: An Empirical Mixed-Methods Study on Educational Equity Human Capital Development Zohaib Hassan Sain; Anni Rahimah; Nurulannisa Abdullah; Nurhana Fakhriyah Imtinan; Chanda Chansa Thelma
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1486

Abstract

The rapid integration of Artificial Intelligence (AI) into lifelong learning creates a range of opportunities and challenges, especially for educational equity and human capital development. AI applications in educational environments have the potential to enable personalized learning, expand access, and improve outcomes. Yet, these advantages raise important issues regarding privacy, bias, and human oversight in education. The main aim of this research is to investigate how AI can support educational equality in lifelong learning environments. The research aims to recognise and respond to ethical issues, such as bias, privacy, and implications for autonomy in learning. The study employs a mixed-methods design that includes quantitative surveys, qualitative interviews, and document analysis to assess these concerns. Descriptive statistics and regression analysis are employed for quantitative data, while thematic analysis is conducted for qualitative data to identify major patterns related to ethical considerations. Results demonstrate that AI integration is significantly and positively associated with perceived educational equity (β​=​0.45, p​=​0.001), while Data Privacy Concern (β​=​−0.30, p​=​0.003) and Algorithmic Bias Concern (β​=​−0.25, p​=​0.042) show significant negative moderating effects. Qualitative analysis identifies regulatory need (90%), data privacy (75%), and algorithmic bias (60%) as dominant stakeholder concerns. The study underscores the imperative of robust ethical governance frameworks to ensure AI technologies advance educational equity equitably and sustainably
Application of Simple Additive Weighting Method in Web-Based Student Learning Interest Detection Using Digital Questionnaires Kukuh Daruningsih; Widi Hastomo
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1508

Abstract

Learning interest plays a vital role in shaping students' motivation and academic achievement, particularly at the junior high school level, where students are required to determine their educational pathways. However, the major selection process in schools is often based on subjective judgments rather than systematic evaluation of students' interests, which may lead to inappropriate recommendations. This study develops a web-based decision support system to identify student learning interests and support major selection using a structured approach. Data were collected from 18 Grade IX students through a digital questionnaire designed based on predefined learning interest criteria. The Simple Additive Weighting (SAW) method is applied to calculate preference scores and generate major recommendations. The research methodology includes requirements analysis, system design, implementation, and testing. System functionality was evaluated using Black Box Testing to ensure that all features operate correctly. The results show that the system successfully processes questionnaire responses and produces consistent recommendations based on SAW calculations. Black Box Testing confirms that all functions operate as expected without errors. The proposed system demonstrates that integrating digital questionnaires with SAW can provide a structured, transparent, and efficient decision support tool for junior high school major selection. Although this system is currently limited to the junior high school level, it has the potential to be further developed for broader educational levels
Development of an Interactive E-Module for Wired and Wireless Network Technology Learning at SMK Negeri 1 Sijunjung Hasana Fiddaraini; Heri Mulyono; Bernediv Nurdin
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1544

Abstract

This study aimed to develop an interactive electronic module (e-module) for the Wired and Wireless Network Technology subject at SMK Negeri 1 Sijunjung and to evaluate its validity and practicality as a digital learning medium in vocational education. The research employed a Research and Development (R&D) approach using the ADDIE model, which consists of analysis, design, development, implementation, and evaluation stages. The participants involved media experts, subject-matter experts, teachers, and Grade XI students of the Computer and Network Engineering program. Data were collected through observations, interviews, validation questionnaires, practicality questionnaires, and student response questionnaires. The collected data were analyzed using descriptive quantitative techniques.The developed e-module was created using Adobe Animate in HTML5 format and integrated multimedia components such as text, images, videos, audio, animations, and interactive exercises. The validation results showed that the e-module obtained a score of 84.03% from media experts and 93.30% from subject-matter experts, indicating that the module was categorized as very valid. The practicality test results showed scores of 93.75% from teachers and 88.52% from students, which were classified as very practical. In addition, student responses indicated that the e-module supported learning engagement, independent learning, and understanding of networking concepts more effectively. These findings indicate that the developed e-module is feasible and practical for use as a digital learning medium in vocational education and can support more flexible and interactive learning activities.
A Systematic Literature Review of AHP–TOPSIS Applications in Decision Support Systems Durrotul Mufidah Inafah; Tri Rijanto; Yeni Anistyasari
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1483

Abstract

Decision Support Systems (DSS) increasingly utilize Multi-Criteria Decision Making (MCDM) methods to address complex decision problems involving multiple and often conflicting criteria. Among these methods, the AHP–TOPSIS hybrid approach has gained significant attention due to its ability to combine structured criteria weighting with objective alternative ranking. However, existing studies are generally fragmented and domain-specific, limiting a comprehensive understanding of its application trends and methodological patterns. This study aims to provide a systematic synthesis of AHP–TOPSIS applications in DSS through a Systematic Literature Review (SLR). The review follows PRISMA guidelines and analyzes 20 journal articles published between 2014 and 2025, sourced from Google Scholar, Garuda, and SINTA databases, with limited support from Scopus-indexed references. The results show a notable increase in publications after 2020, indicating the growing importance of data-driven and transparent decision-making in the context of Industry 4.0 and 5.0. The majority of studies focus on selection and ranking problems, particularly in education, human resource management, and industrial applications. A consistent methodological pattern is observed, where AHP is used for determining criteria weights and TOPSIS is applied for ranking alternatives. This study contributes by providing a cross-domain perspective on the use of AHP–TOPSIS, highlighting its methodological consistency, adaptability, and practical relevance. The findings also suggest opportunities for future research, particularly in integrating AHP–TOPSIS with advanced approaches such as fuzzy logic and intelligent systems to address uncertainty and dynamic decision environments
Determination of Tuberculosis Risk Clusters Based on Health Factors in East Java Using Fuzzy Gustafson Kessel Naila Qalbi; Mohammad Idhom; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1579

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis and remains a major public health problem in Indonesia, particularly in East Java Province. This study aims to group tuberculosis risk levels across 38 districts/cities in East Java Province based on health determinants using the Fuzzy Gustafson-Kessel (FGK) clustering method. The data were obtained from the Central Statistics Agency of East Java Province and the East Java Provincial Health Office in 2024, using four main variables: the number of Diabetes Mellitus (DM) patients, malnourished toddlers, Human Immunodeficiency Virus (HIV) patients, and productive-age active smokers. The FGK method was applied because it can form elliptical clusters through the Mahalanobis distance approach, making it suitable for data with non-homogeneous distribution characteristics. The optimal number of clusters was determined using the Modified Partition Coefficient (MPC). The results show that the four-cluster solution achieved the highest MPC value of 0,987 indicating good cluster partition quality. These four clusters represent tuberculosis risk groups categorized as low priority, medium priority, and high priority across districts/cities in East Java Province. The clustering results can serve as a basis for determining intervention priorities and supporting more targeted public health policy planning
Spatial Clustering of Dengue Hemorrhagic Fever Risk Areas in Lamongan Regency Using the SKATER Method Cahya Eka Melati; Muhammad Nasrudin; Mohammad Idhom
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1580

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health problem in Lamongan Regency, Indonesia, with unequal distribution across sub-districts. This study aims to identify and classify DHF-prone areas using Spatial ‘K’luster Analysis by Tree Edge Removal (SKATER), a graph-based spatial clustering method. The study used cross-sectional secondary data at the sub-district level, including DHF Incidence Rate (IR), population density, rainfall, and percentage of adequate sanitation. Spatial autocorrelation was analyzed using Moran’s Index, followed by weighted graph construction and Minimum Spanning Tree (MST) partitioning for cluster formation. Cluster quality was evaluated using the Sum of Squared Deviations (SSD) and Between-Cluster Sum of Squares (BSS). The Moran’s I results showed significant spatial autocorrelation for all variables (p < 0.05). The five-cluster configuration produced better clustering performance, with lower SSD (49.84) and higher BSS (58.16) compared to the three-cluster configuration (SSD = 86.18; BSS = 21.82). The results revealed spatial variations in DHF vulnerability, ranging from very low to very high categories. These findings indicate that the SKATER method effectively identifies geographically contiguous and homogeneous DHF-prone areas to support spatially targeted DHF control planning in Lamongan Regency
Price Dynamics and Financial Risk Analysis A Neural Hierarchical Time-Series Forecasting Approach Vannesa Nathania; Aviolla Terza Damaliana; Shindi Shella May Wara
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1583

Abstract

The highly volatile nature of cryptocurrency prices often causes conventional predictive models to fail in capturing complex nonlinear patterns. This study integrates the Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) deep learning model with nonparametric Historical Simulation Value-at-Risk (VaR) method for price forecasting and risk analysis. Using univariate data on daily Ethereum closing prices from January 1, 2021, to January 31, 2025 (N = 1,491 observations), the out-of-sample evaluation was executed using a rolling cross-validation scheme initiated testing from a cut-off point in April 2024 through December 2024, where each evaluation window was set for the next 30 days. The research results show that the N-HiTS model can predict price dynamics with high accuracy, achieving an MAPE of 3.25%, an MAE of 107.825, an RMSE of 136.83, and directional accuracy of 48.28%. Risk analysis using historical simulation yielded a VaR of -6.23% at a 95% confidence level.
A Multimodal Digital Health Prototype for Real-Time Student Stress Monitoring Using Internet of Things and Artificial Intelligence Salsabilla Mulyabudiman; Agmisyaniah Agmisyaniah; Sebtina Cinta Anugrahini; Ajeng Azzahroh; Annisa Zhafira Adhya
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1658

Abstract

Academic stress has become an important concern among university students because of its potential impact on physical health, psychological well-being, and academic performance. Conventional stress-assessment approaches are often limited by subjective evaluation and the lack of continuous monitoring capabilities. This study aimed to design SynBioSense, a conceptual framework and prototype for student stress monitoring that integrates Internet of Things and Artificial Intelligence technologies. A Design Research approach was employed to develop the system architecture, monitoring workflow, conceptual verification framework, and mobile application prototype. The proposed framework was designed to integrate multimodal physiological sensing, cloud-based infrastructure, Artificial Intelligence-assisted analysis, and mobile-based visualization within a unified digital-health architecture. The study resulted in conceptual system architecture, workflow model, and user-interface prototype that illustrated how monitoring, visualization, recommendation, and user-support functionalities could be integrated into a student-oriented platform. The study contributed an integrated conceptual design that may serve as a foundation for future implementation, validation, and deployment of digital stress-monitoring systems in higher-education environments. Future research should focus on system implementation, Artificial Intelligence model development, and empirical evaluation involving university students
Integrated QR Code-Based Cafe Ordering with Point of Sale and Electronic Receipt Hasna Anggun Nafisa; Erna Hudianti Pujiarini
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1552

Abstract

The rapid growth of information technology has driven digital transformation in various business sectors, including the culinary industry. One innovation that is widely adopted is the use of a QR Code-based ordering system to increase service efficiency and transaction transparency. This research aims to design and visualize a QR Code-based cafe ordering system that is integrated with a Point of Sale (POS) system and electronic receipt features. System development follows the Waterfall methodology, which consists of the stages of requirements analysis, system design, implementation, and functional testing. The result of this research is a web-based prototype that represents the digital ordering process, starting from scanning the QR Code, selecting the menu, sending the order, confirming payment by the cashier, and accessing digital receipts. Functional testing shows that all key system features operate according to specified requirements. Even though the system is still a prototype and has not been integrated with a database or back-end system, this system is able to describe system workflow and user interactions effectively. The proposed prototype can be an academic reference and foundation for the development of a QR Code-based cafe ordering system in the future.
Robust Large-Scale Poverty Prioritization Using a-Cut Fuzzy AHP and Fuzzy WASPAS Hauzan Hanifah Zahra; Amri Muhaimin; Sugiarto
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1560

Abstract

Prioritizing poverty alleviation programs remains challenging due to multidimensional indicators and uncertainty in expert judgment. This study purposes a robust decision support framework by integrating -cut Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (Fuzzy WASPAS) for large-scale poverty prioritization. The -cut mechanism was incorporated into the Fuzzy AHP weighting process to improve flexibility and robustness under uncertainty, while Fuzzy WASPAS was employed to rank 20,000 household alternatives based on 14 poverty indicators derived from DTKS and BPS Data. Sensitivity analysis was conducted using several  values to evaluate ranking stability under varying confidence levels. The results demonstrate that the proposed framework produces highly stable rankings, with an average maximum rank shift of 143 positions (0.7%) and a median shift of 81 positions (0.4%) across all alternatives. Futhermore, the model achieved an average Spearman rank correlation of 0.9996, indicating strong consistency in poverty prioritization outcomes despite variations in fuzzy defuzzification parameters. The findings confirm that the integration of α-cut Fuzzy AHP and Fuzzy WASPAS provides a reliable and robust approach for evidence-based poverty targeting and social assistance allocation. The proposed framework can support policymakers in improving the accuracy, transparency, and consistency of poverty intervention strategies.

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