<![CDATA[This study develops a decision support system to enhance the objectivity and reliability of selecting starting volleyball players, particularly for the spiker position, where traditional selection processes are often subjective and inconsistent. The research addresses the limitation of single-method decision models by integrating the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) into a unified multi-criteria decision-making framework. AHP is employed to derive consistent and structured criterion weights, while SAW is used to generate a transparent ranking of player alternatives. Seven evaluation criteria were used, including passing, service, smash, block, teamwork, body mass index (BMI), and speed. Data were collected through structured observations and expert evaluations involving the team coach and founder. The results indicate that the smash criterion has the highest weight (0.4138), confirming its dominant role in spiker performance. The final ranking shows that Neng Irma Sukmayani achieved the highest score (0.9678), followed by Siti Karlina (0.9602) and Nova Amelia Putri (0.9040). Compared to subjective selection approaches, the proposed system provides a measurable and reproducible evaluation process, improving decision transparency and consistency. The integration of AHP and SAW contributes by reducing weighting bias while maintaining computational simplicity in ranking. The system was implemented using PHP and MySQL and validated through black-box testing, demonstrating stable functionality across all features. This study contributes both theoretically, by strengthening hybrid MCDM applications in sports analytics, and practically, by providing a scalable decision support model for athlete selection.]]>
Copyrights © 2026
