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All Journal JSAI (Journal Scientific and Applied Informatics) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Abdidas GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Informanika Journal Of World Science Jurnal Pengabdian Mandiri SATIN - Sains dan Teknologi Informasi Jurnal Pengabdian Masyarakat Digismantech (Jurnal Program Studi Bisnis Digital) Oikonomia : Journal of Management Economics and Accounting
Lili Indah Sari
Institut Sains Dan Bisnis Atma Luhur
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Explainable XGBoost-Based Prediction of Student Reading Interest Using Digital Learning and Reading Behavior Indicators Delpiah Wahyuningsih; Heki Aprianto; Lili Indah Sari; Wishnu Aribowo Probonegoro; Sri Rahayu; Serly Oktarina
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1698

Abstract

Reading interest is an important indicator of academic engagement and lifelong learning, yet its relationship with digital learning behavior remains underexplored in higher education. This study proposes an explainable machine learning framework to predict student reading interest using digital learning behavior, reading habits, and demographic characteristics. Data were collected through self-reported questionnaires from 300 Indonesian university students. Reading interest was formulated as a binary target variable, and three feature scenarios were evaluated: digital behavior only (S1), reading behavior only (S2), and an integrated model combining all features (S3). The dataset was divided using an 80:20 stratified train–test split, with SMOTE applied only to the training data to address class imbalance while preventing information leakage. S3 achieved the highest ROC-AUC (91.92%) and cross-validation F1-score (86.62% ± 3.34%), while S1 obtained the same test accuracy (84.29%) and a slightly higher test F1-score (85.33%). SHAP analysis provided interpretability by identifying daily reading duration and monthly book count as key predictors. These findings indicate that multidimensional behavioral indicators can support early student-engagement screening systems. However, results from a single institutional context require cautious interpretation.
Co-Authors Ade Septryanti Amri Ardiana Delpiah Wahyuningsih Ellya Helmud Heki Aprianto Hidayat Febiansyah Jery Jamhari Lilo Renaldi Maxrizal Maya Saftari Parlia Romadaina Parlia Romadiana Parlia Romadiana R Burham Isnanto R.Ferry Bakti. A Serly Oktarina Sri Rahayu Sujono Supardi Supardi Supardi Wishnu Aribowo Probonegeoro Wishnu Aribowo Probonegoro Wishnu Probonegoro Wishu Ariwibowo Probonegoro