<![CDATA[Timely graduation is one of the indicators of success in higher education administration. Students in the Master of Information Technology Program are required to complete all academic obligations, including fulfilling study-credit requirements and completing a thesis, within the normal study period of two years or four semesters. This study aims to apply the Random Forest algorithm to classify timely graduation among Master of Information Technology students from the 2023 cohort at Universitas Pembangunan Panca Budi, analyze the model performance, and identify the academic attributes that contribute most to the classification results. The study used academic data from 27 students, including total completed credits, Cumulative Grade Point Average (CGPA), and graduation status. Graduation status was categorized into timely graduation and delayed graduation. The data were processed through preprocessing, label encoding, an 80:20 training-testing data split using stratified split, and Random Forest model development with 100 decision trees. The results showed that the model achieved an accuracy of 83.33%, precision of 100%, recall of 80.00%, and an F1-score of 88.89%. The feature importance analysis indicated that CGPA contributed 100% to the classification results, while completed credits contributed 0% because all students completed the same number of credits, namely 48 credits. Therefore, the Random Forest algorithm can be used to classify students’ timely graduation based on the available academic data.]]>
