Sistem Rekomendasi Siswa Berprestasi berdasarkan Nilai Akademik dan Non-Akademik SMA Brawijaya Smart School - Vito Ramadhan; Ahmad Afif Supianto; Djoko Pramono (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Brawijaya Smart School High School has various types of student achievement. However, among the various types of achievement, there were no achievement that used Academic and Non-Academic aspects as a main reference in the selection. Teachers find it difficult to use Academic and Non-Academic value data due to the nature of the two values. Based on existing problems, a system is needed that is able to process combined data of Academic and Non-Academic value to determine outstanding students based on these two aspects. K-Nearest Neighbor (KNN) is a data mining algorithm that can be used for the classification process of outstanding students. As for the sorting of the classification results with many attributes, Simple Additive Weighting (SAW) algorithm is used. The attribute used consists of the average grade of students from semester 1-5 and students plus poin. The validation test produce a score of 100% Valid. The output of this study is a system that can provide recommendations of outstanding students by implementing the website-based KNN and SAW algorithm. And the results of the User Acceptance Testing (UAT) has a value of 86%.