Student Grouping Based on Grades and Attendance Using K-Means
DOI:
https://doi.org/10.55123/jomlai.v5i1.7283Kata Kunci:
K-Means, Clustering, Data Mining, Students, VisualizationAbstrak
Student grouping based on academic performance is needed to support decision-making in more targeted academic guidance programs. This research implemented K-Means Clustering algorithm to group students based on academic scores and attendance rates. The dataset consisted of 50 student samples with score and attendance percentage attributes ranging from 0-100. Optimal cluster determination used Elbow Method and Silhouette Score with K values varying from 2 to 6. Experimental results showed K=3 produced optimal separation with highest Silhouette Score of 0.72 and WCSS 8,230. Three clusters formed represented high-achieving students (30%), average-performing students (40%), and students requiring special attention (30%). The algorithm converged in average of 8-12 iterations with 90% consistency on multiple runs. Correlation analysis showed very strong relationship between scores and attendance (r=0.89). Interactive visualization system was developed using React.js and Recharts to facilitate result interpretation. This research provided practical contribution in form of clustering framework for early warning identification of at-risk students and academic intervention program recommendations.
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Hak Cipta (c) 2026 Theresya Simanjuntak, Jelita Astrid Gulo, Sardo Pardingotan Sipayung
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