Penerapan Data Mining Menggunakan K-Means Clustering Dalam Mengelompokkan Tingkat Kesulitan Mata Pelajaran
- Fahrul Fauji Institut Teknologi dan Bisnis Asia, Malang, Indonesia
-
Lia Farokhah
*
Institut Teknologi dan Bisnis Asia, Malang,
Indonesia
(*) Corresponding Author
Abstract
SMA Kertanegara Malang is committed to providing and improving the quality of education to its students. One of the main challenges that is often faced is that there are certain subjects that students consider difficult. Difficult subjects often become an obstacle for students to achieve satisfactory grades. One approach that can be taken is Data Mining. The analysis technique used is the K-Means algorithm, Clustering method. The Elbow method helps in determining the right number of clusters for the data that has been processed. The results of the Elbow method in this research are the most optimal cluster value, namely K= 3 based on the Within-Cluster Sum Of Square value of 104.5298167. The data used are the report cards of class The aim of this research is to determine what groups of subjects are considered difficult. The research results after 163 data were transformed into 30 data obtained 3 optimal clusters using the Elbow method, namely Cluster_1 with a difficult subject category containing 8 subjects. Cluster_2 with the medium difficulty level lesson category contains 10 subjects. Cluster_3 with the easy lesson category contains 12 subjects. The results of this grouping can be used by teachers at SMA Kertanegara Malang to provide more assistance to students, especially in subjects that are categorized as difficult.
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