<![CDATA[School performance can be measured through student completion rates, which is a key indicator. Low graduation rates indicate problems in the education and learning system, which require timely intervention to prevent students from not completing their education. Therefore, predicting graduation rates is crucial for schools in order to determine students who are likely to not complete their education, so as to provide early assistance to improve their academic performance. This research is urgent because by understanding and predicting student completion, schools can allocate resources more effectively to support at-risk students, with the ultimate goal of improving graduation rates and overall school performance. This research uses random forest algorithm with graduation dataset. The distribution of training and test data selection uses stratified random sampling method to ensure a balanced representation of each class generated. The Random Forest model was successfully obtained through training and model evaluation using test data, showing an accuracy of 1.0 or equivalent to 100%. The use of the random forest algorithm on student graduation datasets can be an effective approach in supporting timely graduation prediction due to its high accuracy and the model's ability to recognize both graduating and non-passing students.]]>
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