Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach


  • Teuku Rizky Noviandy Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23771, Indonesia
  • Zahriah Zahriah Department of Architecture and Urban Planning, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Erkata Yandri Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia
  • Zulkarnain Jalil Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Muhammad Yusuf Department of Pharmacy, STIKES Assyifa Aceh, Aceh 23242, Indonesia
  • Nur Intan Saidaah Mohamed Yusof Faculty of Pharmacy, Universiti Teknologi MARA Selangor, Bandar Puncak Alam 42300, Malaysia
  • Andi Lala School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Rinaldi Idroes School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia



Machine learning, Academic excellence, Dropout risks, Stacked classifier, Educational data


Education is important for societal advancement and individual empowerment, providing opportunities, developing essential skills, and breaking cycles of poverty. Nonetheless, the path to educational success is marred by challenges such as achieving academic excellence and preventing student dropouts. Early identification of students at risk of dropping out or those likely to excel academically can significantly enhance educational outcomes through tailored interventions. Traditional methods often fall short in precision and foresight for effective early detection. While previous studies have utilized machine learning to predict student performance, the potential for more sophisticated ensemble methods, such as stacked classifiers, remains largely untapped in educational contexts. This study develops a stacked classifier integrating the predictive strengths of LightGBM, Random Forest, and logistic regression. The model achieved an accuracy of 80.23%, with precision, recall, and F1-score of 79.09%, 80.23%, and 79.20%, respectively, surpassing the performance of the individual models tested. These results underscore the stacked classifier's enhanced predictive capability and transformative potential in educational settings. By accurately identifying students at risk and those likely to achieve academic excellence early, educational institutions can better allocate resources and design targeted interventions. This approach optimizes educational outcomes and supports informed policymaking, fostering environments conducive to student success.


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How to Cite

Noviandy, T. R., Zahriah, Z., Yandri, E., Jalil, Z., Yusuf, M., Mohamed Yusof, N. I. S., Lala, A., & Idroes, R. (2024). Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach. Journal of Educational Management and Learning, 2(1), 28–34.