DATA MINING MEMPREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN METODE K-NEAREST NEIGHBORS (KNN) STUDI KASUS UNIVERSITAS PGRI MAHADEWA INDONESIA

Abstract

Graduation is a significant milestone in education, and it is a crucial assessment factor for ensuring higher education accreditation. The K-Nearest Neighbor (KNN) algorithm classifies objects based on learning data, with a minimum and maximum number of training datasets. The algorithm normalizes patterns, calculates Euclidean distance, votes from the smallest euclidean distance, and determines the classification results. The Student Graduation Prediction Model uses the KNN method to help assess students' graduation accuracy and accreditation.