<![CDATA[The primary parameter for measuring higher education quality, which also has a crucial impact on the accreditation process, is the percentage of students graduating on time. However, the reality on the ground shows that many students face obstacles in completing their studies within the ideal timeframe. Therefore, a data-driven strategy is needed to project students' chances of graduation early. This research aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying the potential for on-time graduation. The data utilized included 161 entries from the Informatics Study Program, class of 2022, at the University of Muhammadiyah Sidoarjo. The attributes analyzed were divided into academic and non-academic factors, including gender, first-semester social studies grades (IPS), GPA, PKMU (Community Service Program) graduation score and status, BQ and Ibadah scores, and accumulated SKEK points. The research process went through several phases: preprocessing, class labeling, model development, and performance evaluation through a confusion matrix and 5-fold cross-validation. The test was validated by separating the training and test data into ratios of 70:30, 80:20, and 90:10. Based on the test results, the C4.5 algorithm achieved a peak accuracy of 100% across all ratio scenarios, with an average cross-validation accuracy of 96.88%. Meanwhile, Naïve Bayes achieved a maximum accuracy of 94.13% with an average cross-validation of 93.00%. These findings indicate that the C4.5 algorithm has superior performance on this specific dataset. The output of this predictive model is expected to serve as an objective basis for institutions in establishing proactive academic policies.]]>
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