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Prescriptive Learning Analytics for Student Dropout: Integrating Temporal Velocity and Counterfactual Explanations in Longitudinal Data Nurul Hidayat; Lasmedi Afuan; Helmi Roichatul Jannah
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15920

Abstract

Student dropout in higher education remains a persistent socioeconomic challenge, yet many predictive models reported in the literature are methodologically compromised by randomized cross-validation schemes that introduce temporal data leakage and artificially inflate predictive performance. This study proposes a longitudinal prescriptive learning analytics framework integrating three complementary methodological components: a Leave-One-Cohort-Out (LOCO) temporal validation protocol, a hybrid SMOTE-ENN class balancing strategy, and temporal velocity feature engineering derived from Learning Management System (LMS) behavioral trajectories. The framework was evaluated on a longitudinal dataset comprising 464,739 enrollment records and 77 features. Five predictive algorithms—XGBoost, LightGBM, CatBoost, Random Forest, and Logistic Regression—were comparatively assessed on a strictly isolated blind holdout cohort (2022), with CatBoost emerging as the champion estimator, achieving a PR-AUC of 0.8859, a Macro F1-Score of 0.9143, and the lowest Brier Score (0.0221), thereby demonstrating superior calibration and discriminative capability under severe class imbalance (93:7 ratio). Comprehensive ablation analysis revealed that temporal velocity features function not merely as additive predictors, but as a structural prerequisite enabling Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN) to generate high-quality synthetic boundary instances; removing these features reduced minority-class precision from 0.8302 to 0.6721. To operationalize predictive outputs into actionable intervention pathways, Diverse Counterfactual Explanations (DiCE) were implemented under a three-tier causal constraint architecture on 96 borderline high-risk students, generating 384 feasible intervention scenarios exclusively targeting forward-looking behavioral velocity metrics without constraint violations. Collectively, these findings advance the paradigm of prescriptive learning analytics by providing educational institutions with interpretable risk diagnostics and operationally feasible intervention guidance grounded in empirically validated behavioral and temporal dynamics.
A SYSTEMATIC REVIEW OF ERP ADOPTION IN SMALL AND MEDIUM ENTERPRISES: CHALLENGES AND OPPORTUNITIES IN DEVELOPING COUNTRIES Helmi Roichatul Jannah; Puteri Awaliatush Shofro; Novi Prisma Yunita
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2889

Abstract

Enterprise Resource Planning (ERP) systems have become a critical enabler for organizational integration, efficiency, and digital transformation. However, the adoption of ERP systems among Small and Medium Enterprises (SMEs) in developing countries remains challenging and uneven. This study presents a systematic literature review of 71 peer-reviewed articles to examine the key challenges and opportunities associated with ERP adoption in SMEs within developing country contexts. Following the TOE (Technology Organization Environment) Framework, articles were screened, and analyzed from major academic databases. The synthesis reveals that technological factors (system complexity, compatibility, and data security), organizational factors (top management support, user competence, and resource constraints), and environmental factors (vendor support, regulatory pressure, and competitive intensity) are the dominant determinants influencing ERP adoption. Despite persistent challenges such as high implementation costs, resistance to change, and limited technical expertise, emerging opportunities-particularly cloud-based ERP, modular implementation strategies, and scalable subscription models-offer promising pathways for SMEs. This review contributes by consolidating fragmented empirical findings and identifying research gaps related to sustainability, post-adoption performance, and longitudinal impacts of ERP adoption in developing economies.
STEMMINDO: A WEB-BASED INDONESIAN STEMMING ENGINE USING ENHANCED CONFIX STRIPPING Novi Prisma Yunita; Helmi Roichatul Jannah
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.521

Abstract

Stemming is an essential preprocessing stage in Natural Language Processing (NLP), particularly for Indonesian, which has complex affixation patterns. Most Indonesian stemming implementations are provided as programming libraries, making them less accessible for beginners, educators, and non-programmer researchers. This study presents Stemmindo, a lightweight web-based Indonesian root word search application implementing the Enhanced Confix Stripping (ECS) algorithm using the Laravel framework. Unlike conventional stemming libraries, the system provides a real-time and modular interface that enables users to explore Indonesian morphological processing without writing program code. The novelty of this research lies in the implementation of ECS within an accessible web-based educational tool. Evaluation was conducted using affixation pattern testing, rule-based testing, and real-text evaluation. Testing on 20 affixation patterns achieved 90% accuracy, while evaluation on 100 words representing 33 derived prefix rules achieved 94% accuracy. After applying failure-handling strategies through exception lists and rule-level accommodations, the accuracy increased to 98%. Real-text evaluation was conducted using 1,742 words collected from Indonesian educational web content. After preprocessing and filtering, 564 unique words were evaluated, of which 366 stemming results were successfully matched with the corpus, while the remaining cases mainly consisted of named entities, noisy input, ambiguous forms, overstemming, and understemming. These findings indicate that the proposed system performs effectively for common Indonesian morphological patterns while remaining practical for educational and experimental NLP usage. Future work includes improving reduplication handling, expanding lexical resources, and enhancing accessibility features.
A SYSTEMATIC REVIEW OF ERP ADOPTION IN SMALL AND MEDIUM ENTERPRISES: CHALLENGES AND OPPORTUNITIES IN DEVELOPING COUNTRIES Helmi Roichatul Jannah; Puteri Awaliatush Shofro; Novi Prisma Yunita
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2889

Abstract

Enterprise Resource Planning (ERP) systems have become a critical enabler for organizational integration, efficiency, and digital transformation. However, the adoption of ERP systems among Small and Medium Enterprises (SMEs) in developing countries remains challenging and uneven. This study presents a systematic literature review of 71 peer-reviewed articles to examine the key challenges and opportunities associated with ERP adoption in SMEs within developing country contexts. Following the TOE (Technology Organization Environment) Framework, articles were screened, and analyzed from major academic databases. The synthesis reveals that technological factors (system complexity, compatibility, and data security), organizational factors (top management support, user competence, and resource constraints), and environmental factors (vendor support, regulatory pressure, and competitive intensity) are the dominant determinants influencing ERP adoption. Despite persistent challenges such as high implementation costs, resistance to change, and limited technical expertise, emerging opportunities-particularly cloud-based ERP, modular implementation strategies, and scalable subscription models-offer promising pathways for SMEs. This review contributes by consolidating fragmented empirical findings and identifying research gaps related to sustainability, post-adoption performance, and longitudinal impacts of ERP adoption in developing economies.