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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 93 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.

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