Balancing Student Specialization Class Placement Based on Interests and Talents Using K-Means Clustering and Genetic Algorithm - Islamy, Chaidir Chalaf; Oktaviansyah, Muhammad Andika (Journal of Applied Informatics and Computing, 2025)
Student specialization placement in Indonesian secondary schools often produces imbalanced class distributions and misalignment between student interests and assigned tracks. This study develops a hybrid optimization system combining K-Means clustering and Genetic Algorithm (GA) to allocate 133 tenth-grade students from SMAN 1 Ngimbang into four specialization classes (Science, Mixed-Science, Mixed-Social, Social) while balancing operational constraints. Initial K-Means clustering (k=4, n_init=100) achieved a Silhouette Score of 0.287 but yielded severely imbalanced distribution (10, 51, 48, 24 students). GA optimization (population=300, generations=150, crossover=70%, mutation=10%, elitism=10%) with multi-component fitness function incorporating cosine similarity, distribution penalty, movement penalty, and entropy produced balanced classes (31, 35, 35, 32 students) within the 30-35 target range. Post-optimization metrics showed 73.7% retention rate, average match score of 0.792, entropy of 0.482, and execution time of 47.8 seconds. The Silhouette Score decreased to 0.080, reflecting an acceptable trade-off between cluster purity and operational feasibility. Sensitivity analysis confirmed weight configuration robustness. This system demonstrates practical applicability for real-time school implementation, reducing distribution gap by 90.2% while maintaining individual-class compatibility.