A Machine Learning-Based Framework for Optimizing Sports Talent Identification and Development

This research aims to set a new standard in sports talent discovery, effectively addressing existing gaps and enhancingtalent scouting in Indonesia. The validity and reliability of the instrument were assessed using a single-visit, crosssectional descriptive design in the study. The statistical analysis makes use of the Measuring of Sampling Adequacy(MSA) and the Kaiser-Meyer-Olkin (KMO) test. SPSS Version 23 was used to create the validity test, which evaluates theassessment tool's reliability. The reliability test used in this study was the Cronbach's alpha coefficient. The requireddependability score is 0.70 or higher. Reliability is therefore regarded as popular. The research's conclusions offer freshperspectives on trustworthy and legitimate tools. An Indonesian sports instrument with two components—biomotor 91.4& 92 and anthropometric 93.4 & 93—is the end product of this research.