Modeling Car Driver Selection Using an Integrated Hybrid AHP-MOORA Approach - Resiwara Cinantya Kusuma Kawekas; Heru Purwanto (Journal of Applied Informatics and Computing, 2026)
In transport management, traditional driver selection methods suffer from subjectivity and reactiveness, which directly increase traffic hazards, asset liabilities, and corporate operational expenditures. To address human cognitive limitations in concurrently analyzing complex multi criteria (physical, personality, and technical facets), this research deploys a computational Decision Support System (DSS) to secure objective, measurable quality standards and operational safety. The primary objective is to develop an integrated AHP-MOORA framework to optimize the recruitment pipeline. By leveraging AHP for criteria prioritization and MOORA for alternative evaluation, the proposed system yields optimized driver recommendations tailored to specific user requirements. The model incorporates seven distinct criteria: working experience, license classification, age, personality, educational background, residential proximity, and examination scores. Performance testing via a confusion matrix reveals that the constructed DSS delivers high robustness, achieving 87.5% Accuracy, 83.3% Recall, and a 90.9% F1-Score. Remarkably, the system attains a flawless 100% Precision rate, substantiating its optimal ability to yield highly targeted driver recommendations while entirely eliminating false-positive classifications.