Clustering Courses Based On Student Grades Using K-Means Algorithm With Elbow Method For Centroid Determination

Abstract

Students who have taken courses will receive grades from a performance index with a weight of 0 to 4. The amount of historical student data, particularly on course grades, has the potential to discover new insights. Still, course grades are closed data and are only for academic and management purposes. The research aims to a grouping of courses with high average grades. In this research, the clustering of courses using the k-means clustering algorithm using the elbow method to determine the centroid. Based on the Sum of Squares calculation, the optimal number of clusters with k=2 was obtained. The clustering results produced cluster 1 with a centroid value of 2.686 and 15 members and cluster 2 with a centroid value of 3.245 and 40 members. It can be concluded from this research that the members of cluster 2 are a group of courses with high average grades.