Explainable hybrid machine learning for predicting student enrollment decisions in Indonesian private universities

Abstract

Student enrollment prediction is an important challenge for private universities because accurate predictions can support admission planning, marketing strategies, and institutional resource allocation. However, conventional prediction models may provide high predictive performance while offering limited insight into the factors underlying students’ enrollment decisions. This study proposes an explainable hybrid machine learning approach to predict student enrollment decisions in Indonesian private universities and to identify the factors that contribute most strongly to the prediction outcomes. The study uses student enrollment data collected from Universitas Wirahusada Medan and Universitas Audi Indonesia. The proposed approach combines feature selection and multiple machine learning algorithms to improve predictive performance, while explainable artificial intelligence (XAI) using Shapley additive explanations (SHAP) is employed to interpret the model predictions. The models are evaluated using accuracy, precision, recall, F1-score, receiver operating characteristic - area under the curve (ROC-AUC), and confusion matrix analysis. The results show that the proposed hybrid approach provides competitive predictive performance compared with individual machine learning models. The SHAP analysis further reveals the relative contribution and direction of the most influential features in determining student enrollment decisions. These findings demonstrate that combining predictive modeling with explainability can provide not only accurate enrollment predictions but also actionable insights for university decision-makers. The study contributes an integrated approach for supporting data-driven student recruitment and enrollment management in Indonesian private higher education institutions.