CLUSTERING COLLEGE ENTRANCE EXAM SCORES USING K-MEANS METHOD

Surya Rahmadani; Putu Sugiartawan; I Gede Duta Kharisma Putra; I Kadek Adi Rian Nugraha; I Gede Adnyana Putra; Ketut Dionanda Sutrisna

Authors

Surya Rahmadani Institut Bisnis dan Teknologi Indonesia
Putu Sugiartawan Institut Bisnis dan Teknologi Indonesia
I Gede Duta Kharisma Putra Institut Bisnis dan Teknologi Indonesia
I Kadek Adi Rian Nugraha Institut Bisnis dan Teknologi Indonesia
I Gede Adnyana Putra
Ketut Dionanda Sutrisna Institut Bisnis dan Teknologi Indonesia

Keywords:

Clustering, K-Means, Exam Scores, Silhouette Score

Abstract

The new student selection process in higher education faces the challenge of managing an ever-increasing amount of exam data, which is often inefficient and error-prone. This research implements the K-Means clustering method to group the entrance exam scores of prospective students, aiming to improve efficiency and accuracy in decision-making. Data collected from different types of selection tests were prepared through cleaning and normalization before applying the K-Means algorithm. The analysis results showed that the method successfully grouped prospective students into three clusters with clear academic characteristics, and produced a Silhouette Score of 0.72, indicating good clustering quality. The findings provide important insights for universities in planning study programs and developing more adaptive educational policies.

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