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All Journal Jurnal Computech & Bisnis (e-Journal) Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Science and Mathematics Education International Journal of Computer Technology and Science Bulletin of Intelligent Machines and Algorithms
Setiana, Elia
Universitas Informatika Dan Bisnis Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Physics Social Sciences Other

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Ensemble Learning for Early Warning Systems in Higher Education: A Comparative Study of Student Attrition Arifudin, Muhamad Achya; Setiana, Elia; Nugraha, Arif Bakti
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i3.19

Abstract

Student attrition poses a substantial challenge to higher education institutions, affecting their reputation and financial sustainability. Conventional single machine learning models often exhibit limited sensitivity when analyzing educational data, which is typically marked by severe class imbalance favoring graduating students over dropouts. This study introduces an Early Warning System based on a Hybrid Stacking Ensemble framework to improve student attrition prediction. The approach leverages complementary biases from Bagging and Boosting as base learners, which are then combined using a Logistic Regression meta-learner to refine prediction weights. To counteract class imbalance and majority-class bias, the Synthetic Minority Over-sampling Technique was employed during preprocessing. Empirical evaluations reveal that the Hybrid Stacking Ensemble attains a classification accuracy of 88.81% and a Recall of 80.99%, surpassing standalone models and other ensemble methods. Feature importance rankings highlight second-semester academic performance and administrative-financial factors—particularly tuition payment punctuality—as key dropout predictors. These results affirm the value of integrating diverse classifiers to discern intricate, nonlinear student behavior patterns. In essence, this work establishes a reliable, evidence-based framework enabling administrators to shift from reactive to proactive, precision-targeted strategies that foster student retention and institutional success.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Akbar, Imannudin Arifudin, Muhamad Achya Budiman Budiman Budiman Budiman Budiman Chairul Habibi Danestiara, Venia R Dirham Triyadi Dirham Triyadi Graha Prakarsa Hamzah, Encep Ikhsan Nur, Muhammad Kaunang, Valencia Claudia Jennifer Marwondo Muhammad Fajar Romadhon Muhammad Rizki Ramadhan Mukhammad Restu Febriansyah Putra Muliasari, Lina Nugraha, Arif Bakti Nur Alamsyah NUR ALAMSYAH Nur Alamsyah, Nur R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah Rakhman A, R. Yadi Ramadhan, M. Rizki Reni Nursyanti Reni Nursyanti Reni Nursyanti Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Sardjono Syahira Putri Himmaniah Taufik Hidayah Teofilus Hendrawan, Yesaya Titan Parama Yoga Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Valencia Claudia Jennifer Kaunang Venia Restreva Danestiara Wiyanudin