Penerapan Algoritma K-Nearest Neighbor dengan Euclidean Distance untuk Menentukan Kelompok Uang Kuliah Tunggal Mahasiswa

Fenny Purwani, Ragil Tri Wahyudi, Irfan Dwi Jaya

Abstract


Single tuition fee or called UKT is the amount of tuition fee determined based on the student's economic ability. In its application, there are still many students who object to the UKT group that is obtained. Therefore, the university must apply the right and accurate method in determined the UKT group. This study aims to obtain the result of student’s UKT group classification using the K-Nearest Neighbor (KNN) algorithm with Euclidean Distance calculation and determine the accuracy of the algorithm with the optimal k value. This study used a quantitative method with a descriptive approach. The data collection techniques used are interviews, literature study, and documentation. The data that has been collected is 1,650 student’s UKT verification data for 2019-2021 which be processed with data mining using the RStudio software. The results showed that the classification with KNN can be applied in determined student’s UKT. With data testing many as 320 students, 23 students were determined to get UKT I, 149 UKT II, 129 UKT III, 32 UKT IV, and 2 students got UKT V. The accuracy of the algorithm is 87.58% in the Good Classification category. The optimal k for KNN obtained with K-Fold Cross Validation is k=1.


Keywords


data mining; classification; k-nearest neighbor; euclidean distance

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References


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DOI: https://doi.org/10.29408/edumatic.v6i2.6547

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