Early Warning System for Detecting Student Dropouts Using the Random Forest Algorithm at SMKS Alhuda

Muhammad Khoerulrijjal Salsabila Januarta; Nuk Ghurroh Setyoningrum

doi:10.59934/jaiea.v5i3.2385

Authors

Muhammad Khoerulrijjal Salsabila Januarta cipasung university
Nuk Ghurroh Setyoningrum cipasung university

DOI:

https://doi.org/10.59934/jaiea.v5i3.2385

Keywords:

Dropout Prediction, Early Warning, Machine Learning, Random Forest, Vocational Education

Abstract

The dropout rate in vocational high schools poses a serious challenge that requires an objective early-detection system. This study aims to optimize a model for predicting student dropout risk by utilizing a supervised learning approach. The study uses multivariate data covering student demographic attributes, academic achievement, behavior, and financial history. The Random Forest algorithm was implemented to classify student risk levels into Safe, Caution, and Danger categories to support preventive decision-making. Model performance testing using a confusion matrix showed an accuracy rate of 99%, with a recall of 100% in the High-Risk category, demonstrating the algorithm’s effectiveness in accurately identifying high-risk students. These findings contribute to the development of more precise early detection methods in educational settings.

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