PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION

Abstract

Completion of studies of students in a timely manner is one measure of the quality of higher education, as well as in finding a job. Anticipation that can be done is by predicting graduation, with these predictions do evaluation efforts in organizing lectures at the faculty or study program. The data in this study is the student data Gorontalo State University Faculty of Education and Faculty of Engineering from 2008 until 2012. From 5104 the total number of records is done with attribute data sorting empty, so the existing data into 2312 record. The algorithm used in this study is a K-Nearest Neighbor which will then be optimized using Particle Swarm Optimization. By using the technique Fold Cross Validation on K-Nearest Neighbor algorithm produces the highest accuracy 88.58 on the value of k = 14. The next test using particle swarm optimization algorithm to get the highest accuracy on the population size = 10 with accuracy of 89.14%.