<![CDATA[Student dropout is a serious problem that can affect the quality of education and operational efficiency of higher education institutions. Early prediction of potential students who will dropout is essential to develop appropriate intervention strategies, so as to increase graduation rates and reduce the negative impact on academic continuity. A better model for student dropout prediction becomes an objective of this research. The method used in this research is to improve the performance of machine learning models through the selection of optimal hyperparameters. The research methodology consists of several stages, including data preprocessing, handling imbalanced data, model training, and performance evaluation. There are three machine learning models used in this research, namely XGBoost, AdaBoost, and Random Forest. The selection of optimal hyperparameter values is carried out using the Random Search and Grid Search methods. Model evaluation is conducted using k-fold cross-validation and multiple evaluation metrics, including accuracy, precision, recall, and F1-score. As part of the important results, the combination of XGBoost and Random Search produced the best performance with 91.18% accuracy, indicating that hyperparameter optimization significantly improves predictive performance. The findings of this research explicitly contribute to the field of informatics, particularly educational data mining, and provide insights for educational institutions to identify high-risk dropout students more accurately.]]>
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