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INDONESIA
Indonesian Journal of Information System
Published by Universitas Atma Jaya Yogyakarta
ISSN : 26230119     EISSN : 26232308     DOI : -
Core Subject : Science,
Computer Science & IT
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Articles 209 Documents
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A Systematic Literature Review on Multi-Criteria Decision Analysis and Machine Learning for Decision-Making in Digital Payment Investment Rahmat Rambe; Lukman Abdurrahman; Hanif Fakhrurroja
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.13797

Abstract

The growth of financial technology has strengthened the role of digital payments indriving the digital economy and influencing investment decision-making. This developmentcreates both opportunities and challenges for investors in evaluating digital payment investments.Multi-Criteria Decision Analysis (MCDA) supports structured evaluation across multiple criteria,while Machine Learning (ML) enhances predictive capabilities using historical and market data.However, studies integrating MCDA and ML remain limited and unsystematic. This studyconducts a systematic literature review (SLR) based on the PRISMA framework, analyzingpublications from 2019 to 2024 related to the application of MCDA and ML in digital paymentinvestment and decision-making. The results indicate an increasing research trend, with commonlyapplied MCDA methods such as AHP, TOPSIS, and PROMETHEE, and ML algorithms includingSupport Vector Machine and Gradient Boosting. This review identifies research gaps and providesdirections for future studies and practical investment strategies in the digital payment sector. Keywords: Digital Payment; Investment; MCDA; Machine Learning; Systematic Literature Review.
DevQuizzer: An Adaptive Game-Based Learning System for Personalized Programming Instruction Femi Elegbeleye; Isong Bassey
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14181

Abstract

Over the years, balancing personalized support with learner motivation has proven difficult in programming education. Traditional instructional approaches seldom adapt to individual needs, often resulting in uneven engagement and cognitive overload. Gamified settings can enhance motivation, but many lack adaptive mechanisms informed by motivational frameworks such as Self-Determination Theory (SDT). As a result, few systems combine reinforcement learning (RL) with probabilistic learner modelling to deliver scalable, personalized learning pathways. This paper proposes DevQuizzer, an adaptive game-based learning (AGBL) system designed to support personalized programming instruction. The system combines RL and Bayesian Networks (BNs) models to dynamically adjust task difficulty, feedback, and learning pathways according to learner performance and engagement. A dual-mode instructional design: code-first and theory-first, accommodates various learning preferences, while gamified elements sustain motivation. We evaluated AGBL with students from five universities using a mixed-methods approach that combined quantitative measures of usability, engagement, and adaptivity with qualitative insights into learner experiences. Results confirmed the system’s reliability and construct validity, with learners reporting high satisfaction, autonomy, and perceived learning gains. The findings show the potential of hybrid RL-BN models to provide transparent, interpretable, and scalable adaptive learning environments for programming education.
Optimized Multi-Agent Reinforcement Learning for Dynamic Open RAN Slicing Femi Elegbeleye
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14219

Abstract

Fifth generation (5G) networks enable radio access network (RAN) slicing, allowing a shared physical infrastructure to be partitioned into multiple logical slices, each tailored to meet heterogeneous service requirements. Slice tenants specify service-level agreements (SLAs) that must be satisfied; however, inefficient resource allocation mechanisms frequently result in SLA violations. Recent studies have adopted constrained multi-agent reinforcement learning (MARL) to dynamically allocate RAN resources, yet these approaches often remain inefficient due to reward misalignment with SLA objectives and unstable exploration in high-dimensional state–action spaces. This paper presents a reward-function–centric analysis of constrained MARL for 5G RAN slicing, examining how different reward designs affect learning efficiency and SLA compliance. Based on this analysis, we propose heuristic-guided reward shaping strategies that explicitly align the learning objective with SLA satisfaction and resource utilization efficiency. In addition, we introduce a state-space optimization technique that aggregates local agent states into a weighted global state representation, thereby reducing state dimensionality while preserving critical SLA-related information. Extensive experimental evaluations demonstrate that the proposed approach achieves a 60% reduction in SLA violations compared to an existing constrained MARL model and a 46% reduction compared to a state-of-the-art model-based reinforcement learning approach. Furthermore, the proposed solution converges faster and requires less training time and computational resources, highlighting its effectiveness and practical applicability for efficient 5G RAN slicing.
Artificial Intelligence for Climate Resilience and Risk Management in Mining: A PRISMA-Based Review and Conceptual Framework Fadzai Dzehonye; Sibusisiwe Dube
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14268

Abstract

Mining operations are increasingly facing climate-related risks, including extreme rainfall, flooding, slope instability, and infrastructure failure. These climate change risks usually occur in open mining environments. Traditional risk management methods in mining are less reactive and less effective at managing the currently rapidly escalating climate-related risks. Artificial Intelligence (AI) offers promising capabilities for improving climate resilience and risk management through predictive, data-driven, and adaptive solutions. This review shows how recent empirical studies have explored how AI is used to promote climate resilience and risk management in the mining environment. Using the PRISMA framework, peer-reviewed studies from 2020 to 2025 were analysed to identify key application areas, popular AI techniques, analytical frameworks, and implementation challenges. The PRISMA-guided search across Scopus, Web of Science, and ScienceDirect screened 580 records and identified 16 empirical studies; however, coverage of African mines was limited, and few papers linked AI predictions to routine HSE decision workflows. Results indicate that AI is primarily applied for geotechnical risk monitoring, flood forecasting, and hazard detection via machine learning, deep learning, and remote sensing. Nonetheless, AI adoption remains fragmented, hazard-specific, and hindered by data shortages, model interpretability, and organisational barriers. This review, therefore, highlights the need for integrated, interpretable, and operationally embedded AI systems to support proactive, long-term climate resilience in mining.
A Decision-Making Framework for Academic Information System Maintenance Prioritization using AHP and Tiered Resource Allocation Hilyah Magdalena; Ade Septryanti
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14272

Abstract

University academic information systems face increasing digital demands, but maintenance strategies often lack systematic prioritization, leading to inefficient resource allocation and neglect of critical features. This study aims to develop a structured framework to prioritize maintenance of academic system features at ISB Atma Luhur, ensuring optimal resource distribution and system reliability. A quantitative study using the Analytic Hierarchy Process (AHP) was conducted in four phases: (1) inventorying 237 features across eight academic units and defining eight evaluation criteria through expert consultation; (2) applying AHP with 11 domain experts using Expert Choice software for criteria weighting and consistency validation (CR < 0.10); (3) calculating priority scores with PSₖ = Σ(wᵢ × sₖᵢ); and (4) designing a 24-month action plan with different resource allocations. The analysis identified two high-priority systems (BAAK: 9.14, SIAD: 8.12), three medium-priority systems (Finance: 6.85, Student Affairs: 6.79, Final Project: 6.23), and three low-priority systems (PMB: 5.18, Library: 4.12, General: 3.25). Preservation of Academic Values ​​is the most important criterion (weight: 0.258). The recommended allocation is 60% for high-priority systems, 30% for medium-priority systems, and 10% for low-priority systems. The AHP-based framework successfully achieved its objectives, providing a data-driven tool for maintenance management. Future studies should explore integration with predictive analytics to proactively improve system reliability
Digital Transformation Success: A Systematic Review of Organizational Readiness and Capability Drivers Nokulunga X Mashwama; Courage Matobobo; Prince DN Ncube
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14396

Abstract

Digital transformation success remains inconsistently explained across fragmented streams of research, which variously emphasise leadership, technology, culture, or organisational capabilities in isolation. This study addresses this limitation through a systematic review that synthesises literature on organisational readiness and capability drivers shaping digital transformation outcomes. Guided by the PRISMA protocol, 52 peer-reviewed studies published between 2015 and 2025 were analysed using qualitative thematic synthesis. The findings identify eight interrelated domains: strategic leadership, organisational readiness, dynamic capabilities, human capital, digital culture, technological infrastructure, resource orchestration, and ecosystem alignment, which are consistently associated with digital transformation outcomes. The analysis suggests that organisational readiness is a dynamic, multidimensional condition that shapes how strategic intent is translated into implementation processes. By integrating previously fragmented constructs, the study proposes an integrative framework that illustrates how organisational readiness interacts with key capability drivers in digital transformation contexts. The study contributes a consolidated conceptual perspective and an integrative framework to support future empirical research, while offering practical insights for organisations seeking to strengthen readiness and align capabilities for sustained digital transformation success.
SENTAKU: A Web-Based Tournament Management System with Zero-Shot LLM Referee Evaluation for Shorinji Kempo Troy Troy; Kathryn Widiyanti; Agni Saraswati
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14529

Abstract

This paper presents SENTAKU (Sistem Elektronik Pertandingan Kenshi Unggul), a referee analytics platform for Shorinji Kempo built on a PHP-based competition management system. The core contribution is a structured referee analytics framework that (i) computes seven per-referee statistical indicators from the raw embu score matrix, (ii) submits them with a structured prompt to Llama 3.3 70B via the Groq API, and (iii) audits the generated report through an objective content audit of coverage, numerical grounding, and section completeness. Two research questions are addressed: whether the framework surfaces referee patterns invisible to manual workflows, and whether a zero-shot LLM with structured context produces text faithful to the underlying statistics. Deployment at POPDA DIY 2026 (5 contingents, 53 randori athletes, 10 judges, 111 matches) surfaced component-level calibration differences and same-regency scoring patterns unattainable through paper workflows. However, the LLM report named only six of ten judges, cited no numerical value from the prompt, and omitted two of five requested sections, while introducing no fabricated content. These findings indicate that absence of hallucination alone is insufficient to guarantee faithful LLM reporting: zero-shot prompting can fail through systematic under-utilisation of numerical input even when explicitly required. Schema-enforced decoding is motivated as the next step
Innovative Hybrid CNN Approach for Leaf Disease Detection in Rice Plants Evanita; Maria Angela Kartawidjaja; Dandy Wibowo; Rizal Ramli; Dwi Nining Lestari
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14665

Abstract

Rice production in Indonesia faces persistent threats from foliar diseases that reduce yield and grain quality. Manual inspection remains impractical for smallholder farmers managing large cultivation areas. This study proposes a hybrid deep learning framework combining DenseNet-169 and ResNet-50 architectures for classifying six rice leaf conditions: Bacterial Blight, Blast, Brown Spot, Health, Hispa, and Leaf Smut. The model was trained on 609 images and validated on 152 images. The proposed architecture achieved 94.08% validation accuracy with a macro-averaged F1-score of 0.93. Class-wise analysis revealed perfect precision and recall for Health and Hispa classes, while Brown Spot presented the greatest classification challenge with 75% recall. Comparative analysis with recent literature demonstrates that the hybrid approach achieves competitive performance while maintaining moderate computational requirements suitable for eventual edge deployment. The confusion matrix reveals specific misclassification patterns between Brown Spot and Leaf Smut, indicating directions for future dataset expansion and architectural refinement.
Integrating the NIST 800-30 Risk Management Framework with Penetration Testing: A Case Study of Web-Based Training Information Systems in a Cybersecurity Company Yohanes Dewantara Marpaung; Halim Budi Santoso; Erick Kurniawan; Gabriel Indra Widi Tamtama; Abdul Karim
Indonesian Journal of Information Systems Vol. 9 No. 1 (2026): August 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v9i1.14877

Abstract

Web application security has become a paramount concern due to the escalating frequency of cyber threats targeting internet-based information systems. Web developers must prioritize risk management, with a particular emphasis on integrating risk identification within the information security management system. Companies specializing in information security management must exercise caution when deploying web-based applications, as information security is a critical issue that can substantially affect a company's reputation. However, previous research has frequently failed to address this issue comprehensively. Consequently, this study seeks to investigate the integration of a risk management framework with penetration testing. The amalgamation of penetration testing with NIST SP 800-30 is expected to provide a comprehensive risk assessment. The penetration testing was conducted using a grey-box testing methodology, guided by OWASP WSTG v4.2 and augmented by CVSS v3.1 for severity measurement. Subsequently, NIST SP 800-30 was employed as the risk management framework. The grey-box testing was performed on a recently deployed web-based training management system. As a result, four vulnerabilities were identified and verified through Proof of Concept: SQL Injection (High), Blind Stores XSS (Critical), Unrestricted file upload enabling remote code execution (Critical), and Brute Force Attack (High). A comprehensive risk identification and mitigation process was then conducted for these vulnerabilities. This study also provides improvement suggestions to aid in mitigating the identified vulnerabilities. This research introduces a novel approach by integrating penetration testing with risk management frameworks to enhance the effectiveness of risk management in web-based applications

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