Implementasi Algoritma KNN untuk Memprediksi Performa Siswa Sekolah - Dhita, I Made Ryan Prana; Mastrika Giri, Gst. Ayu Vida (Jurnal Nasional Teknologi Informasi dan Aplikasnya; Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023DO - 10.24843/JNATIA.2023.v01.i03.p06, 2023)
One of the factors that influences students' graduation rates is their performance in learning. Predicting graduation rates based on student performance has the benefit of analyzing academically underperforming students and providing support to students who face difficulties in the learning process. There are several factors to consider in predicting students' graduation rates, such as academic grades, attitudes, and social factors. However, these factors alone are not sufficient to effectively predict students' performance, and educators also struggle to identify which factors affect students' performance.To predict the performance of school students, the K-Nearest Neighbor (KNN) method is utilized. The K-Nearest Neighbor method is often used in classifying students' performance due to its simplicity and ability to produce significant and competitive results. In this research, the prediction of students' graduation rates is carried out using the KNN method.The results of implementing the prediction of students' performance using the KNN method can serve as a reference for students to improve their achievements and assist educators in considering future teaching materials.