Smart Campus Dropout Prediction: Hybrid Features and Ensemble Approach - M Safii; Adli Abdillah Nababan; Husain Husain (International Journal of Engineering, Science and Information Technology, 2025)
The issue of the high number of students dropping out of college is a major concern in higher education, especially in the smart campus ecosystem. This research aims to design a prediction system for students who are at risk of dropping out by integrating hybrid feature selection methods and ensemble learning that leverage academic data and students' digital footprints. The initial process of model development involves data cleaning and the selection of important features through a combination approach using filter-based methods (mutual information) and recursive feature elimination. A classification model is then designed using the XGBoost and Random Forest algorithms. The testing was conducted using a secondary dataset that included variables such as participation in discussions, attendance rates, interaction with learning materials, and academic achievement. The results of testing with the XGBoost model showed a satisfactory accuracy level, with an F1 score of 0.77 and a ROC AUC of 0.89. The confusion matrix recorded 67 correct predictions for students who graduated and 17 correct predictions for students who dropped out, with a total of 12 misclassifications. These findings suggest that the combination of hybrid feature selection strategies and XGBoost can produce sufficiently accurate predictions of student dropouts and has the potential to be utilized as an early warning system in the governance of a more flexible and responsive smart campus.