Balancing Student Specialization Class Placement Based on Interests and Talents Using K-Means Clustering and Genetic Algorithm
DOI:
https://doi.org/10.30871/jaic.v9i6.10425Keywords:
Genetic Algorithm, K-Means Clustering, RIASEC, School Optimization, Student PlacementAbstract
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.
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