Hybrid Heuristic Algorithms for Optimizing University Graduate-Job Matching: A Quantitative Study in Indonesia's 2025 Labor Market - Nidauddin, Ikbal; Prabowo, Kresno Murti; Alim, Abdullah (Informatika, Universitas Jenderal Soedirman, 2026)
University graduate unemployment in Indonesia reached critical levels with 1,010,652 unemployed graduates in 2025 (BPS data), representing approximately 15% of national unemployment due to severe skills mismatch between education outcomes and labor market demands. This research develops and validates a novel hybrid heuristic algorithm integrating Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) with adaptive diversity-based switching mechanisms to optimize graduate-job matching through multi-objective competency profile alignment. The quantitative experimental study collected data from 200 university graduates across five academic disciplines and 5 major recruiting companies through structured surveys and competency assessments. The proposed GA-PSO-SA hybrid algorithm with adaptive switching achieved 92.4% matching accuracy (35% improvement over traditional methods), 42% faster convergence compared to single algorithms (10.6s vs. 18.4s for pure GA), and solution quality of 8.9/10. Statistical validation through paired t-tests demonstrated highly significant improvements (p < 0.001, Cohen's d > 2.0) across all comparisons. The system successfully reduces average job search duration by 40% (from 6+ months to 3.6 months) and improves graduate placement success rates by 28%. This research contributes a theoretically-grounded and empirically-validated intelligent recommendation system addressing Indonesia's graduate employment crisis through computational optimization, with implications for national workforce development and recruitment efficiency enhancement.