Optimizing Fair and Efficient Group Formation in Community Service Program Using Particle Swarm Optimization

Abstract

Purpose: The rapid expansion and administrative complexity of community service programs (Kuliah Kerja Nyata/KKN) have made manual group formation increasingly inefficient, inconsistent, and prone to imbalance. This creates an urgent need for an automated, fair, and reliable optimization method capable of handling large-scale grouping constraints. This study aims to evaluate the performance of the Particle Swarm Optimization (PSO) algorithm in generating optimal KKN group formations, focusing on computational efficiency, convergence behavior, and solution quality.

Methods: PSO was implemented to form 27 KKN groups using 10 independent runs. Performance metrics included execution time, optimal iteration counts, initial fitness scores, and best final scores. Each run was analyzed to observe convergence patterns and stagnation behaviors.

Result: The results indicate that PSO is highly efficient, with very fast execution times and rapid convergence, often reaching optimal solutions in the first iteration. However, performance varied: some groups achieved low optimal scores (95–97), while many stagnated at extremely high scores (100000.0) with no improvement. This shows that PSO’s effectiveness depends heavily on problem characteristics and initialization.

Novelty: This study identifies and explains stagnation patterns in PSO when applied to discrete, constraint-heavy academic group formation problems, an area rarely examined in prior research. The analysis provides insight into PSO’s strengths and limitations and highlights the need for improved parameter tuning and initialization strategies. The findings serve as a foundation for developing more robust optimization approaches for fair and efficient KKN group formation.