Pendekatan Clustering untuk Menganalisis Efisiensi dan Kinerja Mahasiswa Berdasarkan Data Menerapkan Metode K-Means

 (*)Amelia Rahmadhani Mail (Politeknik Caltex Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: October 1, 2022; Published: October 25, 2022


The purpose of this study is to cluster the efficiency and performance of students. This is because the academic community is currently faced with several challenges in terms of analyzing and evaluating the progress of a student's academic achievement. In the real world, classifying student performance is a scientifically challenging task. Recently, several studies have applied cluster analysis to evaluate student outcomes and used statistical techniques to divide their scores in relation to student performance. This approach, however, is not efficient. In this study, we combined two techniques, namely k-mean and elbow clustering algorithm to evaluate student performance. Based on this combination, the performance results will be more accurate in analyzing and evaluating the progress of student performance, the application of the Elbow method according to this study gives the best number of clusters to 3, and when the K-Means method is applied, data is generated that the number of students is 73 students, from 4 repetitions. There are 3 clusters, namely the category of "Achievable", "Potential for Achievement", and "Less Achievement", with the results of the "Achievable" cluster as many as 34 students with a percentage of 47.22%, the cluster "Potential for Achievement" as many as 24 students with a percentage of 33.33 %, and the "Less Achievement" cluster as many as 15 students with a percentage of 19.45%.


K-Means Technique; Elbow Technique; Clustering Technique; Data Mining; Academic Performance

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