<![CDATA[This research aims to develop a predictive model using machine learning techniques to forecast cum laude graduations within a university. Machine learning algorithms are utilized for classification to enable such predictions. The research results demonstrate the effectiveness of the model in predicting cum laude graduation, thereby providing opportunities for the university to enhance the overall quality of graduates and address potential declines in graduation standards. Predictions regarding the number of cum laude students are made in this study to assist decision-making processes among university stakeholders. By leveraging machine learning techniques, institutions can anticipate and support students in achieving cum laude honours, ultimately leading to an improvement in the overall quality of graduates. In this study, three machine learning algorithms—Naïve Bayes, random forest, and C4.5—are compared for predicting student graduation with cum laude honours. The results of the study show that, for the considered case, the best performance was achieved by the Naïve Bayes algorithm with 87.60% accuracy, 86.70% precision, 92.10% recall, and 89.30% F1-score. In addition, the Naïve Bayes algorithm also obtained the lowest computational time compared to other algorithms.]]>
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