Analysis of K-Means Clustering Implementation for New Student Segmentation Based on School of Origin and Study Program
DOI:
https://doi.org/10.21512/emacsjournal.v8i2.14486Keywords:
K-Means Clustering, Student Segmentation, Elbow Method, Silhouette Score, Davies–Bouldin Index, PCA 2DAbstract
Increasing Competition among higher education institutions requires a deeper understanding of prospective students characteristics to develop more effective promotional strategies and student admission policies. The K-Means Clustering Algorithm is used in this study to divide recently admitted students into discrete groups according to their chosen course of study, kind of previous school, and place of origin. The Davies-Bouldin Index (DBI) and the Silhouette Score were used to assess the clustering performance's efficacy, and the Elbow Method was used to determine the ideal number of clusters. The Experimental result indicated that the optimal segmentation consisted of two clusters. The first cluster was primarily composed of students graduating from general senior high schools (SMA), who tended to enroll in highly demanded health-related study programs. Conversely, the second cluster exhibited a more heterogeneous distribution concerning study program preferences and was predominantly constituted of students hailing from Islamic senior high schools (MA) and vocational high schools (SMK). The cluster analysis has been found to give a Silhouette Score of 0.1774 and a Davies–Bouldin Index of 2.1806, meaning that although the separation between clusters is still inadequate, it is good enough to reveal some distinct patterns in the traits of the new students admitted. The results of this research can be used as a useful benchmark for developing marketing and partnership policies with secondary schools and making data-based decisions-making in the new student admission process.
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