Utilizing the Random Forest Method for Predicting Student Dropout Risk in Madrasah Environments

Mahsun, Muhammad, Hariyadi, M. Amin ORCID: https://orcid.org/0000-0001-9327-7604 and Harini, Sri ORCID: https://orcid.org/0000-0001-9664-027X (2025) Utilizing the Random Forest Method for Predicting Student Dropout Risk in Madrasah Environments. Journal of Information Systems and Informatics, 7 (4). pp. 3434-3453. ISSN 2656-4882

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Abstract

The phenomenon of school dropout is a crucial issue that
negatively impacts the performance of educational institutions,
social stability, and human resource development. Therefore, early
detection of high-risk students is a strategic preventative measure.
This research aims to develop an accurate predictive model using a
Machine Learning approach, by conducting a comparative
evaluation of the Random Forest algorithm. The research dataset
originates from Madrasah Miftahul Ulum, Sidogiri Islamic Boarding
School, and comprises 1,763 student records. The experimental
results indicate that Random Forest provides the best performance
with an accuracy of 82%, precision of 83.8%, recall of 79%, and an
F1-score of 80%. The model was trained using 4 scenarios with
Random state configurations of 40, 60, and 75 to ensure the
consistency of the evaluation results. These metrics indicate that
the model performs in a balanced manner between sensitivity and prediction accuracy, and is effective in identifying internal and
external factors contributing to the risk of dropout. Based on the
model evaluation results, Random Forest is recommended as a
decision support instrument to facilitate more targeted
interventions, such as academic support, economic aid, or student
counseling guidance. This research has a limitation because the
model was only tested at the Madrasah Miftahul Ulum, Sidogiri
Islamic Boarding School institution, thus its application in other
contexts needs further study.

Item Type:	Journal Article
Keywords:	Student Dropout Prediction, Random Forest, Machine Learning, Madrasah Miftahul Ulum
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Mokhamad Amin Hariyadi
Date Deposited:	13 Jul 2026 09:21

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