Aplikasi K-Nearest Neighbor Dengan Fungsi Jarak Gower Dalam Klasifikasi Kelulusan Mahasiswa

Studi Kasus : Mahasiswa Program Studi Statistika, Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Mulawarman

Abstract

The results of the reaccreditation of the Statistics Study Program, Mulawarman University in 2019 remain accredited B. One of the assessment indicators used in reaccreditation is the student's timely graduation status. Therefore, it is necessary to predict the graduation status of Statistics students, Mulawarman University.. The prediction method used in this research is K-Nearest Neighbor (K-NN). K-NN is a classification method based on studying previously classified data. This method is very easy to understand, easy to applied and also non-parametric method, so that no certain assumptions are needed in the process. The independent variables used in this study were student profiles, including gender, regional origin, cumulative Grade Point Average (GPA) and single tuition fee. The dependent variable in this study is the graduation status of students, namely graduating on time and not graduating on time. The data used were students of the Mulawarman University, Statistics Study Program in 2014, 2015, and 2016. The results showed at k = 7 and the distribution of training and testing data with the proportion of 80:20 obtained optimal accuracy of 0,909 with a TPrate of 0.500,  a TNrate. in the amount of 1,000 and AUC value of 0,75 that means fair classification.