CLUSTERING COLLEGE ENTRANCE EXAM SCORES USING K-MEANS METHOD - Rahmadani, Surya; Sugiartawan, Putu; Putra, I Gede Duta Kharisma; Nugraha, I Kadek Adi Rian; I Gede Adnyana Putra; Sutrisna, Ketut Dionanda (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
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.