A Fuzzy Logic Model for Talent Identification and Selection Indonesian Junior Rowing Athletes

Abstract

Background. Investigations in talent identification are mostly based on determining effective parameters, more specifically the determination of norms for Indonesian junior rowing athletes. Objectives. This research aimed to design a smart model in talent identification and selection for rowing athletes based on the weighting of priority criteria generated from the analytic hierarchy process (AHP) of anthropometric, biomotor, psychological, physiological, and technical variables of fuzzy logic. Methods. The method was mixed methods research (MMR), it involves the use of both quantitative and qualitative methods in a study. Furthermore, it selected important criteria through a hierarchical analytical process of anthropometric, biomotor, psychological, physiological, and technical variables. The norms of elite rowing junior athletes aged 16-18 years were used as a comparative index. Furthermore, the smart model is designed based on fuzzy logic using MATLAB software. Results. The athletes were categorized into unmatched, semi-matched, matched, brilliant, and rare groups. The fuzzy testing of all talent identification and selection criteria for rowing athletes shows that Indonesian rowing male athletes must be in the "brilliant" classification or equal to 88.5% supported by anthropometric criteria, 10.6% supported by physiological criteria, and 89.4% supported by biomotor criteria. Conclusion. Leg height and length, muscle power and leg strength, self-confidence and motivation, specific endurance, catch, drive, and recovery parameters were demonstrated as the main criteria and weighted by the analytic hierarchy process. This smart model analyzes these variables on the norms of elite rowing junior athletes and makes specific results from player talent. Therefore, It is a reliable and useful method for decision-making in talent identification and the selection of rowing athletes at a young age