<![CDATA[This research aims to help Pemalang Islamic SMK in identifying outstanding students and predicting potential scholarship recipients, by utilizing the algorithm, K-Nearest Neighbor (K-NN) in determining students who have the potential to receive scholarships. This research used 100 student data involving attributes such as report card grades, academic achievement, parental responsibilities, parental salary, and participation in organizations. Meanwhile, the testing process is carried out by adding 6 data on potential scholarship recipients to be predicted. The data is then processed and normalized before being applied to the K-NN algorithm. The K-NN steps involve determining the K parameter (number of nearest neighbors), calculating the Euclidean distance, sorting the distance results, and selecting the majority category as a prediction for the new object class. The research results show that the application of the K-NN algorithm with K=3 is successful in providing predictions of outstanding students by considering relevant attributes. This process is carried out with the help of JAVA programming to calculate and analyze data. The research conclusion shows that the K-NN algorithm can be used as an effective prediction tool for classification to determine students who excel and are worthy of receiving scholarships. This research contributes to increasing efficiency and accuracy in the selection of outstanding scholarship recipients in the school environment with an accuracy of 83.33%.]]>
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