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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 832 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.
Does It Matter If the Content Is Generated by Generative AI? A Rapid Literature Review of Academic Integrity, Ethics, and Pedagogical Implications Joshua Ebere Chukwuere
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1544

Abstract

The current wave of adoption and use of generative artificial intelligence (GenAI) across various human sectors, particularly in higher education, has prompted debate and discussion about its applications and ethical implications in higher education. This paper presents insight into the question and ongoing debate: Does it matter if the content is generated or written by GenAI? To contribute to ongoing debates and discussions, this article employed a rapid literature review research methodology to examine literature articles within the timeframe of 2024 to March 2025, involving 59 scientific papers across databases such as Scopus, ResearchGate, and Web of Science, as well as grey literature on Google Scholar. The methodology enabled the researcher to review past technological evolution, draw inferences, and link it to the current wave of AI and GenAI in higher education. The paper found that the evolution of computers and technology has raised many questions, tech stress issues, and discussions, which remain worrisome even to this day. The study found that the ongoing debate and discussions on the impact and ethical implications of GenAI in higher education still confuse both academics and non-academics about the future of GenAI. However, the document provided answers to the ongoing question and highlighted some recommendations for the effective application of GenAI in higher education institutions (HEIs).
A Mixed Adversarial Awareness Technique for Improving Neural Network Defense Moses Apambila Agebure; Sampson Konja; Stephen Akobre; Mohammed Daabo
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1552

Abstract

Neural Network (NN) models, particularly Convolutional Neural Networks (CNNs), have achieved remarkable performance in computer vision tasks but remain highly vulnerable to adversarial attacks. Existing defense techniques mainly focus on detecting adversarial examples and often show limited effectiveness when adversarial perturbations coexist with significant noisy inputs. To address this limitation, this study proposes a Mixed Adversarial Awareness Technique (MAAT) based on kernel density estimation and a Bayesian uncertainty estimator. Kernel density estimation is used to model data manifolds in the input subspace, while the Bayesian uncertainty estimator, inspired by the Dirichlet process, quantifies predictive uncertainty in the input space. The proposed technique was evaluated on three benchmark datasets, CIFAR-10, CIFAR-100, and SVHN, using four adversarial attack schemes, namely FGSM, BIM, JSMA, and C&W, as well as Gaussian noise injection. The LeNet ConvNet model was employed as the test classifier. Experimental results show that MAAT effectively flags adversarial and noisy instances, improving detection performance with AUC values ranging from 0.84 to 0.96, compared with 0.61 to 0.94 achieved by selected state-of-the-art techniques. These findings demonstrate that combining density-based manifold modeling with uncertainty estimation provides a robust defense against mixed adversarial and noisy inputs.
A Meta-Synthesis of Ethics-Aware Software Engineering Practice: A Preliminary Framework Senyeki Milton Marebane; Ernest Mnkandla
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1583

Abstract

The use of evidence-based practice to inform ethical software development is important for all stakeholders to achieve successful software and address the ethical needs of those affected by it. Although research exists on various mechanisms for supporting ethical software development, their integration into a framework that enables expansive ethics awareness and adherence is lacking. This study fills this gap by conducting a qualitative metasynthesis of studies that implemented and evaluated mechanisms to support ethics in software development. Meta-ethnography methodology was followed and supported by the PRISMA reporting guidelines to achieve the study's objective.   Most of the mechanisms identified in the four selected studies are oriented toward supporting the integration of ethics into software development in the artificial intelligence domain, rather than the broader software engineering ethics spectrum. The study contributes a conceptual framework for ethics-aware software engineering practice. The proposed framework requires empirical validation. In addition, Further studies are required to explore other resources reporting on other transformative technologies to expand the framework for improving the software engineering practice.
Modeling Student Learning Profiles from LMS Behavioral Traces Using Big Data Analytics Arief Hidayat; Kusworo Adi; Bayu Surarso
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1588

Abstract

Digital learning environments and Learning Management Systems (LMSs) generate large volumes of time-stamped behavioral traces that can be used to examine how students access resources, navigate course structures, communicate, and approach assessments. Traditional learning-style models often depend on static self-report categories and may not reflect how students actually study in digital courses. This study develops a learning analytics framework for modeling student learning profiles from authentic LMS behavioral traces. The study used a quantitative, non-experimental, longitudinal design based on Canvas LMS interaction data from 15,342 undergraduate students enrolled in 150 large-enrollment courses during the 2023–2024 academic year. More than 500 million raw interaction logs were processed into 24 engineered behavioral features representing temporal engagement, resource access, navigation behavior, interaction activity, and assessment timing. After feature normalization, K-Means clustering was applied, and the optimal cluster solution was selected using the elbow method and average silhouette score. Cluster distinctiveness was examined using one-way analysis of variance, and the association between cluster membership and academic performance category was evaluated using a Chi-squared test. The analysis supported a four-cluster solution. Assessment procrastination and navigation sequentially were the strongest differentiating features.
Sentiment Analysis of Google Maps Reviews on Temple Tourism in Central Java Using IndoBERT Embeddings and BiLSTM Ranggi Praharaningtyas Aji; Primandani Arsi
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1589

Abstract

The rapid growth of user-generated content provides valuable insights into tourists’ perceptions of destinations. This study analyzes sentiment in Google Maps reviews of temple tourism destinations in Central Java using IndoBERT embeddings and a Bidirectional Long Short-Term Memory (BiLSTM) model. A total of 10,714 Indonesian-language reviews were collected through web scraping and processed through preprocessing, pseudo-labeling, embedding generation, and model training. To prevent data leakage, the dataset was divided into stratified training and testing sets, while Random OverSampling (ROS) was applied only to the training data. Since manually annotated labels were unavailable, sentiment categories were generated automatically using a pre-trained IndoBERT classifier. The BiLSTM model achieved 80.25% accuracy on the imbalanced dataset and approximately 95% accuracy against IndoBERT-generated pseudo-labels under balanced training conditions. Improvements in Macro F1-score and balanced accuracy indicate better recognition of minority classes. However, the results should be interpreted cautiously because pseudo-labeling and oversampling may affect performance. Overall, this exploratory study demonstrates the potential of IndoBERT and BiLSTM for Indonesian tourism sentiment analysis while highlighting the need for human-annotated data and stronger validation in future research.
Embracing Augmented Reality and Virtual Reality in South African Basic Education: A Systematic Literature Review Beauty Mugoniwa
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1590

Abstract

This study reviews the integration of augmented reality (AR) and virtual reality (VR) in South African basic education. To investigate how immersive technologies are applied in teaching and learning, focusing on learner outcomes, teacher preparedness and systemic enabling conditions. The systematic literature review (SLR) was guided by Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Systematic Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) frameworks and analysed 22 studies from journal articles, conference papers and theses published between 2018 and 30 September 2025. AR and VR adoption remains uneven and largely experimental, with implementation concentrated in urban and better-resourced facilities, particularly within STEM-related subjects. Browser-based simulations, low-cost virtual laboratories and mobile applications were the most frequently reported technologies, while fully immersive VR deployments were limited due to the financial and infrastructural constraints. Also, immersive technologies can improve experiential learning, conceptual understanding and learner engagement. Adoption is constrained by policy limitations, inadequate infrastructure, unequal access between rural and urban schools and insufficient teacher training. Synthesising fragments evidence and interpreting adoption patterns through the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) and Theory of Planned Behavior (TPB). There is a need for context-sensitive implementation strategies, infrastructural investment and improved teacher development to support AR/VR integration.
AI and Blockchain Adoption in E-Government 3.0: A Systematic Review and Conceptual Framework Nabil Abdallh Abbas Almotawkel; Abdulrahman Mohammed Hussein Obaid; Hamza Ali Abdul Rahman Qasem; Gameil Saad Hamzh Ali
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1591

Abstract

This study presents a systematic review of the adoption of Artificial Intelligence (AI) and Blockchain technologies in E-Government 3.0 within developing countries, based on 99 reviewed studies published between 2020 and 2025. Using the PRISMA 2020 protocol, the review synthesizes existing literature to identify key barriers, opportunities, and emerging governance patterns. The findings suggest that while AI appears to enhance decision-making capabilities, and blockchain is associated with enhanced transparency and data integrity, their successful adoption appears to be constrained by institutional capacity, regulatory frameworks, organizational readiness, and digital trust. The results further indicate that technological factors alone are insufficient without alignment with governance structures and policy environments. Based on the synthesis, this study proposes the SIF-G3.0 conceptual framework, which integrates technological, institutional, regulatory, and trust dimensions as a conceptual model for understanding digital government transformation. The proposed SIF-G3.0 framework remains conceptual and has not yet been empirically validated. The evidence base included heterogeneous sources, including empirical, conceptual, bibliometric, and systematic review studies. The study also highlights critical research gaps and provides directions for future research in hybrid intelligent governance systems.

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