<![CDATA[Timely student graduation is a key indicator of higher education quality and institutional effectiveness. This study aims to optimize student graduation prediction using a Decision Tree algorithm based on Classification and Regression Tree (CART) by integrating academic and non-academic variables. The dataset used in this study is the open-source Student Graduation Dataset obtained from Kaggle, consisting of 379 student records with graduation status as the target variable. The research stages include data preprocessing through mean imputation for missing values, categorical variable transformation, data splitting with an 80:20 ratio, and model optimization using CART hyperparameter tuning as a form of post-pruning. Model performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results show that the optimized CART model achieved an accuracy of 92.1%, with F1-scores above 0.90 for both graduation classes and a balanced trade-off between precision and recall. Furthermore, the resulting decision tree structure is relatively simple and highly interpretable. These findings indicate that the optimized CART algorithm is effective and suitable for implementation as an early warning system to support academic decision-making in higher education institutions.]]>
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