Hybrid Heuristic Algorithms for Optimizing University Graduate-Job Matching: A Quantitative Study in Indonesia's 2025 Labor Market

Ikbal  Nidauddin; Kresno Murti  Prabowo; Abdullah  Alim

doi:10.52436/1.jutif.2026.7.1.5421

Authors

Ikbal Nidauddin Information Management Study Program, Piksi Input Serang Polytechnic, Indonesia
Kresno Murti Prabowo Information Management Study Program, Piksi Input Serang Polytechnic, Indonesia
Abdullah Alim Information Management Study Program, Piksi Input Serang Polytechnic, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.5421

Keywords:

Career Optimization, Genetic Algorithm, Graduate Employment, Heuristic Algorithms, Hybrid Optimization, Job Matching, Labor Market Intelligence, Multi-Objective Optimization, Particle Swarm Optimization, Simulated Annealing

Abstract

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.

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