PENERAPAN METODE K-NEAREST NEIGHBOAR (KNN) DALAM KLASIFIKASI KELULUSAN SISWA - Samsuriah, Samsuriah; Asriyadi, Asriyadi (Yayasan Nusantara Hasana Berdikari, 2026)
Student graduation determination is an important aspect of the educational process that requires objective and accurate decision-making. Along with the development of information technology, the utilization of academic student data can be conducted through data mining approaches to support decision-making processes. This study aims to apply the K-Nearest Neighbor (KNN) method to classify student graduation status based on attendance data and final scores. The dataset consists of five student records as training data and two student records as testing data. The research stages include data preprocessing, distance calculation using Euclidean Distance, and class determination based on the majority of the nearest neighbors with a K value of 3. The results show that student F is classified as graduating because most of its nearest neighbors belong to the graduating class, while student G is classified as not graduating due to a greater number of nearest neighbors from the non-graduating class. Therefore, it can be concluded that the K-Nearest Neighbor (KNN) method is able to provide fairly accurate student graduation classification results and can be used as a decision support tool in the education sector.