Clustering Menggunakan K-Means Untuk Menentukan Mahasiswa Berprestasi (MAWAPRES)

Abstract

To analyze and group data into the same groups, clustering is an effective technique. The aim of this study is to identify groups of students who perform based on IPK data, non-academic performance, organizational participation, and foreign language skills. Furthermore, the K-Means clustering algorithm is used to analyze the data of the classification item. Determining the ideal number of groups or centroids is a clustering process. To group out successful and well-performed students, the most suitable group is selected based on the resulting intra-group and intergroup values. It is expected that the results of this study will provide a better understanding of the characteristics of the student group that performs. Educational institutions can use this data to find potential students and provide appropriate support to improve the quality of education. The research concluded that the K-Means clustering method could be used as an effective way to group students based on their academic achievements. This method can help educational institutions find students who perform to be followed in national and international mawapres.