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All Journal Journal of Information Systems and Informatics
Diana Laily Fithri
Muria Kudus University
Computer Science & IT

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Journal : Journal of Information Systems and Informatics

 1 
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.
Sequential Requirements Prediction in Synthetic Fintech-Like Backlogs Using an Interpretable Hybrid of Transition Rules and Transformer Models Diana Laily Fithri; Soni Adiyono; Muhammad Arifin
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.v8i2.1469

Abstract

Fintech software development is characterized by rapid product iteration and stringent regulatory requirements, resulting in changing requirements as an interrelated set rather than individual elements. In this research, Sequential Requirements Prediction is proposed as a decision support task in Requirements Engineering, where the time-ordered prefix of completed backlog items is used to predict the next likely canonical requirement type as Top-k ranked output. To mitigate noise and inconsistency in backlog data, an LLM-aided semantic normalization step maps diverse requirement descriptions to a closed set of fintech requirement types. The research compares an interpretable rule-based Markov-1 predictor with Transformer-based sequential predictors under a case-level time-aware split. The proposed method is evaluated on a synthetic fintech-like backlog dataset consisting of 900 cases, 5,252 events, and 18 canonical requirement types. The best-performing model, Transformer + normalization + augmentation (M4), achieved Recall@5 = 0.638889, MRR@5 = 0.536806, and NDCG@5 = 0.566667. These results surpassed the rule-based predictor and non-normalized Transformer model. In addition, augmentation further improved Recall@5 from 0.493056 to 0.527778 in the rare-type subset. These findings suggest the methodological promise of the proposed framework for sequence-aware and compliance-conscious backlog analytics in synthetic fintech-like settings.
Co-Authors Fajar Nugraha Muhammad Arifin Soni Adiyono