Pemetaan Riset Identifikasi Bakat Olahraga Berbasis Teknologi di Indonesia: Tinjauan Sistematis Berbasis Scopus
DOI:
https://doi.org/10.69714/2df4zk12Keywords:
artificial intelligence, machine learning, software, sports talent identification, systematic literature reviewAbstract
Talent identification in sports is a critical component of modern athlete development. While artificial intelligence (AI) and machine learning (ML) have transformed talent identification practices globally, Indonesia’s contribution to this field remains limited. This study aims to map the current landscape of technology-based sports talent identification research in Indonesia indexed in Scopus. This study employed Systematic Literature Review (SLR) using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A structured search was conducted in the Scopus database using keywords “sport talent identification” AND “artificial intelligence” OR “machine learning” OR “software”, with inclusion criteria limited to Indonesian-authored articles published between 2016 and 2026. Article selection followed the PICO framework (Population, Intervention, Comparison, Outcome). Four articles met all inclusion criteria. Two articles (50%) employed Research and Development (R&D) approaches developing Android-based and software-based (TIDev) talent identification tools, one articles (25%) utilized a quantitative exploratory design examinging technology policy integration, and one article (25%) applied a machine learning Decision Tree algorithm for football position prediction. All selected technologies demonstrated adequate validity and accuracy, with Cronbach Alpha values of 0.844-0.761, I-CVI score of 0.88-0.93, and a Decision Tree accuracy of 76% with an F1-score of 75%. Notably, no Indonesia Scopus-indexed article specifically implementing artifical intelligence (deep learning or neural networks) for sports talent identification was found. technology-based software and machine learning approaches in Indonesian sports talent identification have demonstrated promosing validity and accuracy. However, the complete absence of AI-based research represents a significant evidence gap and a strategic oppotunity for Indonesian researchers to developp AI-driven talent identification system in support of national sports development programs.
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