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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Information Systems and Informatics
Muhammad Arifin
Muria Kudus University
Computer Science & IT

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Student Performance Classification Using Academic, Socioeconomic, and Digital Behavior Features: A Comparative Study Muhammad Arifin; Fajar Nugraha; Diana Laily Fithri
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1460

Abstract

Accurate prediction of student academic performance is essential for universities seeking to improve learning outcomes and deliver timely, data-driven support. Prior work commonly uses regression to estimate Grade Point Average (GPA), yet numeric predictions can be difficult for administrators to translate into actionable risk levels. This study reframes the task as binary classification, categorizing students as good (GPA ≥ 3.00) or poor (GPA < 3.00) performers. Using 2,423 records from multiple programs at an Indonesian university, we combine academic indicators from the learning management system (login frequency, assignment submission, and forum activity) with socio-economic and digital behavioral variables (parental income, extracurricular participation, study-group involvement, and social media use). Seven machine learning models—Naïve Bayes, Generalized Linear Model, Logistic Regression, Deep Learning, Decision Tree, Random Forest, and Gradient Boosted Trees (GBT)—are benchmarked under a consistent evaluation design. Results indicate that integrating academic, socio-economic, and digital behavioral features improves classification performance, and ensemble methods outperform single, traditional models. GBT yields the best accuracy of 0.75, offering a practical basis for early-warning dashboards and targeted interventions. The study provides comparative evidence from Indonesian higher education and highlights the value of incorporating digital engagement signals alongside conventional academic data for more effective student support services.
AI and Digital Transformation Trends: A Systematic Review with Multi-Criteria Analysis Soni Adiyono; Muhammad Arifin
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1462

Abstract

This research investigates the integration of Artificial Intelligence (AI) and Digital Transformation (DT) as critical enablers of Industry 4.0, highlighting their combined influence in reshaping industrial processes and enhancing operational efficiency. AI technologies, including machine learning, natural language processing, and computer vision, are driving advancements in automation, real-time decision-making, and personalized services across various industries, such as manufacturing, healthcare, and logistics. DT involves the widespread adoption of digital technologies that transform business models, stakeholder interactions, and organizational structures, working synergistically with AI to foster innovation. While existing literature often examines AI and DT in isolation, this study addresses the gap by employing a Systematic Literature Review (SLR) and Multi-Criteria Analysis (MCA) methodology to evaluate research based on academic impact, practical relevance, and sectoral readiness. The analysis reveals emerging trends such as predictive analytics, autonomous systems, and smart manufacturing, with industries like healthcare and retail showing strong adoption, while real estate and legal services remain underexplored. The research examines 46,936 Scopus records and selects 32 studies for analysis. The MCA results underscore the importance of aligning academic research with industrial needs and fostering cross-sector collaboration. Ultimately, this study bridges the gap between theory and practice, offering valuable insights for policymakers, scholars, and practitioners to strengthen competitive advantage in the digital era.
Co-Authors Diana Laily Fithri Fajar Nugraha Soni Adiyono