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

Muhammad Mahsun; M. Amin Hariyadi; Sri Harini

doi:10.63158/journalisi.v7i4.1364

Muhammad Mahsun Maulana Malik Ibrahim State Islamic University of Malang, Indonesia
M. Amin Hariyadi Maulana Malik Ibrahim State Islamic University of Malang, Indonesia
Sri Harini Maulana Malik Ibrahim State Islamic University of Malang, Indonesia

DOI: 10.63158/journalisi.v7i4.1364

Keywords: Student Dropout Prediction, Random Forest, Machine Learning, Madrasah Miftahul Ulum

Abstract

The phenomenon of school dropout represents a crucial issue with negative impacts on educational institution performance, social stability, and national development. Consequently, the early detection of high-risk students constitutes a strategic preventive measure. This research aims to develop an accurate predictive model using a Machine Learning approach. The study employed a comparative evaluation using classification algorithms, with the primary focus being the performance analysis of the Random Forest Classifier. The dataset utilized, comprising 1,763 student records, underwent a rigorous data pre-processing phase, including data cleaning, variable transformation, and class imbalance handling, to ensure high-quality input. The model was trained using a Random Seed configuration of 75 to guarantee experimental reproducibility and consistency in evaluation results. Experimental findings indicate that the Random Forest algorithm provided the best performance, achieving an accuracy of 82.0% and a precision of 83.8%. This superior performance confirms the model's effectiveness in identifying the key determinants of dropout, stemming from both students' internal and external factors. Based on these results, the research recommends the application of Random Forest as a Decision Support System instrument to facilitate targeted interventions, including medical support, economic assistance, and academic counseling. Future research is advised to integrate historical counseling data to further enhance the prediction sensitivity of the model.

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