PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR PADA PROGRAM STUDI SISTEM INFORMASI UNIVERSITAS RIAU

Abstract

Student graduation on time is one of the assessments or benchmarks in the college accreditation process. Higher education accreditation assessment is carried out by the National Accreditation Board for Higher Education. The higher the accreditation value, the higher the quality of the university. Graduation rate is very important, it is necessary to determine student graduation. The study focused on predicting student graduation by data mining using the K-Nearest Neighbor algorithm and the accuracy rate of algorithms measured using the Confusion Matrix. This studied used data from students of the Information Systems Study Program of the University of Riau who had graduated from 2014 to 2016 as many as 148 data. The data was shared using K-FOLD to predict student graduation with the highest accuracy rate of 100% on K-FOLD 9.