Analysis of K-Means and K-Medoids Algorithms for Grouping Academic Values of Prospective New Students in Selection of Campus Scholarship Recipients

Preddy Marpaung

doi:10.55927/fjst.v4i9.224

Authors

Preddy Marpaung University of Mahkota Tricom Unggul

DOI:

https://doi.org/10.55927/fjst.v4i9.224

Keywords:

Clustering, Student Academic Grades, K-Means and K-Medoids Algorithms, Campus Scholarships

Abstract

University of Mahkota Tricom Unggul is one of the private universities (PTS) that prepares various scholarships every year, one of which is the Foundation scholarship. However, so far the University in selecting prospective new students who are eligible to receive Foundation scholarships still uses a conventional model, so it is less effective, efficient, and less professional. As a result, it is less objective in selecting prospective new students in scholarship acceptance. Therefore, an algorithm is needed to group data that is eligible to receive Foundation scholarships using an algorithm. The algorithms used to solve the existing problem are the K-means and Kmedoids algorithms, then analyzed which one is effective in solving the problem. Where both can group data well, but the K-medoids algorithm is more effective in solving than K- Means.

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