Student Grouping Based on Grades and Attendance Using K-Means - Theresya Simanjuntak; Jelita Astrid Gulo; Sardo Pardingotan Sipayung (JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2026)
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.