Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering

Abstract

Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the best number of clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation test, the k-Means algorithm is more optimal than the k-Medoids. So that the cluster of students with the highest average GPA of 3,325 is 401 students.

Keywords

• Data Mining
• Data Pre-Processing
• Validation Test
• Davies Bouldin Index
• Optimal