<![CDATA[- Timely student graduation is an important indicator in assessing the quality of higher education management. However, not all students are able to complete their studies within the prescribed study period, making it necessary to implement data-driven predictive approaches to identify students at risk of delayed graduation. This study aims to compare the performance of the Decision Tree and Naïve Bayes algorithms in classifying timely student graduation based on academic data. The dataset consists of alumni records from the Informatics Engineering Study Program for the 2015–2016 cohorts, totaling 610 valid records after data cleaning and attribute selection. Predictor variables include gender, class type, and Semester Grade Point Index (IPS) from semester 1 to semester 5, while the target variable is graduation status. Model evaluation was conducted using an 80% training and 20% testing split, and performance was measured through a confusion matrix to obtain accuracy, precision, and recall values. The results show that the Decision Tree achieved an accuracy of 69.54%, while Naïve Bayes achieved 68.38%. The 1.16% difference indicates that the Decision Tree performs slightly better for this dataset. These findings suggest that early semester academic performance significantly contributes to predicting timely graduation and can support data-driven academic decision-making.]]>
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