<![CDATA[The high number of students experiencing dropout is a serious challenge that must be addressed by higher education institutions, including STMIK Widya Cipta Dharma. This study aims to propose an early detection system that demonstrates the ability to identify students who are at risk of dropping out based on their academic data. The system is developed using the K-Nearest Neighbor (KNN) algorithm as a classification method to predict the risk of student dropout. The data used includes Grade Point Average (GPA), total credits earned, and the duration of study. The test results show that the KNN algorithm can provide a high level of accuracy in categorizing students into dropout risk levels. This system can serve as a support tool for the institution in determining preventive actions to reduce the number of students at risk of failing to complete their studies and to improve the quality of academic guidance.]]>
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