Penerapan Data Mining untuk Memprediksi Siswa Berprestasi dengan Menggunakan Algoritma K Nearest Neighbor - Sri Widaningsih (JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 2022)
The process of determining outstanding students at SMK Tunas Sinar Mandiri Cianjur is not only determined by academic scores, but considers several non-academic aspects such as gender, extracurricular activeness, presence, and personality. For academic scores, it is taken from the scores of productive subjects in class X and XI. To predict which student excel in class XII, data mining techniques can be applied using the k nearest neighbor (kNN) algorithm with k = 1. The stages of the data mining process follow the stages in Knowledge Discovery on Databases (KDD). This stage starts from selection, preprocessing, transformation, data mining, and evaluation / interpretation. The data transformation process uses the minimum method and the distance formula used is the euclidian distance. Making this data mining application follows the waterfall paradigm. From the results of the analysis and design using Unified Model Langunge (UML), several main pages were produced, namely managing student data, determining variables and variable weights. By applying this data mining techniqu it can make it easier for schools to prepare students who will get scholarships.