Perbandingan Algoritma K-Means dan K-Medoids Dalam Pengelompokan Kelas Untuk Mahasiswa Baru Program Magister

Abstract

This research discusses a comparison of two grouping algorithms, namely K-Means and K-Medoids, in the context of class grouping for new master's program students. Choosing the right clustering algorithm can help universities optimize resource allocation and maximize student learning experiences. K-Means is a popular clustering algorithm, which works by dividing data into a number of homogeneous groups based on the distance between data points and the cluster center. Meanwhile, K-Medoids is a variation of K-Means that uses actual data points as a cluster representation, which makes it more resistant to outliers. This research involves a dataset of new master's program students which includes various attributes, such as entrance exam scores, educational background, and major preferences. The comparison results between K-Means and K-Medoids were carried out by considering clustering evaluation metrics such as SSE (Sum of Squared Errors) and Silhouette Score. Experimental results show that the performance of K-Means and K-Medoids differs depending on the characteristics of the dataset. K-Means tends to produce more homogeneous groups, but is more sensitive to outliers. In contrast, K-Medoids tend to be more stable in dealing with outliers, but may produce less homogeneous groups. Therefore, the selection of an appropriate clustering algorithm should be based on the specific goals and characteristics of the new master's program student population. This research provides valuable insight for colleges in planning the allocation of classes, mentors, and other resources for new students. The right decisions in class grouping can increase student retention, learning satisfaction, and academic success. In addition, this research also stimulates further discussion in combining different clustering methods to achieve more optimal results in grouping classes of new master's program students.