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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 783 Documents
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AI Adoption in Southern African Open and Distance e-Learning: A Systematic Review Tirivashe Mafuhure; Mampilo Phahlane; Charles Mbohwa
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1111

Abstract

The integration of Artificial Intelligence (AI) into Open and Distance e-Learning (ODeL) systems is now a very important aspect in higher and tertiary education worldwide and this also includes Southern Africa. This paper reviewed a total number of 79 peer reviewed studies and other relevant publications from the year 2019 to 2025, examining how AI was being employed to improve teaching, research, learning, and administration processes in ODeL institutions in the Southern Africa region. This research study explored how AI was used address challenges that are peculiar to the Southern African region by looking on aspects to do with high student to instructor ratio, resource constraints, lack of proper expertise, and limited digital infrastructure. Findings from the research study reveal that although AI can offer solutions such as Personalised learning, automation of administrative processes, enhanced learner engagement, and automated assessments, its implementation in most ODeL institutions is hindered by lack of proper infrastructure, lack of expertise, and policy gaps. The review highlighted the need for regional collaboration among Higher Education ODeL institutions, investment in ICT infrastructure, and comprehensive policy development for successful implementation of AI. Findings obtained can assist major stakeholders that include Higher education leaders, policymakers, researchers and students on the potential of AI to transform Open and Distance electronic Learning in Southern Africa.
Student Achievement Prediction Models: A PRISMA-Based Systematic Literature Review Rima Tamara Aldisa; Adian Fatchur Rochim; Agung Triayudi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1526

Abstract

Student achievement prediction has become an important research area in educational data mining because it supports early intervention, academic monitoring, and evidence-based decision-making in educational institutions. This study aims to identify research trends, commonly used methods, predictive variables, and potential research gaps in student achievement prediction models. A Systematic Literature Review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Articles published between 2020 and 2024 were collected from seven reputable databases, namely Scopus, ScienceDirect, IEEE Xplore, SpringerLink, IOP, Wiley, and MDPI. After applying the inclusion and exclusion criteria, 52 articles were selected for final analysis. The findings show that classification-based machine learning methods dominate this research area, with Random Forest being the most frequently used algorithm. Academic data, such as grades, GPA, and attendance, remain the most common predictive variables, while non-academic variables are still rarely explored. This study highlights the need for multi-source data integration, hybrid or ensemble modeling, and broader variable selection to improve prediction accuracy and applicability. The novelty of this study lies in its structured synthesis of recent studies and its proposed direction for developing more comprehensive student achievement prediction models.
Integrating ML with Electronic Fiscal Devices for Real-Time Underpricing Detection in Tanzania Benitho Alphonce Chengula; Judith Leo; Cyril Chimilila
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1582

Abstract

This study aims to develop a machine learning-based tool integrated into Electronic Fiscal Devices (EFDs) to detect underpricing fraud in real time in Tanzania. The motivation for this research arises from the limitations of existing EFD systems, which rely on manual and post-audit mechanisms that are ineffective in identifying fraudulent pricing during transactions. A mixed-methods approach was employed, combining qualitative insights from tax officers with quantitative data collected from traders and buyers. A dataset of 5,000 mobile phone sales transactions collected from Arusha, Dar es Salaam, and Iringa in Tanzania, was pre-processed and used to train and evaluate multiple machine learning models, including Logistic Regression, Support Vector Machine, XGBoost, and Random Forest, using 5-fold cross-validation. The experimental results show that the Random Forest model outperformed other models, achieving an accuracy of 99.6% along with strong precision, recall, and F1-score values. To demonstrate practical applicability, the trained model was further integrated into a prototype EFD environment, where it enabled near real-time fraud detection and generated automated alerts for traders and tax authorities, with geolocation features supporting targeted enforcement. However, the dataset is limited to mobile phone transactions within selected regions of Tanzania, which may affect the generalizability of the findings. The novelty of this study lies in integrating machine learning–based price validation into EFD systems to support proactive detection of underpricing fraud at the point of transaction, thereby enhancing tax compliance and revenue protection.

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