<![CDATA[Timely graduation is a critical indicator in assessing the quality of higher education that directly affects study program accreditation. This study aims to develop a timely graduation prediction model for students of the Informatics Engineering Study Program at the University of Papua using the C4.5 Decision Tree algorithm. The dataset consists of 292 academic records of students from the 2016–2021 cohorts, with eight predictor features comprising Grade Point Average (GPA) and the number of credit units completed from the first to the fourth semester. The results indicate that the C4.5 model combined with the RandomUnderSampler technique achieved the best predictive performance among all tested configurations, with an accuracy of 74.58%, precision of 77.29%, recall of 74.58%, F1-Score of 74.89%, and AUC-ROC of 79.11%. The resulting prediction model is expected to serve as a decision-support instrument for the study program in early identification of students who are at risk of not completing their studies within the stipulated timeframe.]]>
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