Pemanfaatan Artificial Intelligence (AI) untuk Prediksi Fatigue dan Pencegahan Cedera pada Atlet
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Abstract
Physical fatigue remains one of the most consistently documented physiological predictors of injury risk in athletes across all competitive levels. When neuromuscular capacity deteriorates due to unmanaged training load accumulation, the body's ability to stabilize joints and execute precise technical movements degrades significantly. Artificial Intelligence (AI) technologies encompassing machine learning, deep learning, computer vision, and IoT-integrated wearable devices introduce a new paradigm in data-driven training load management. This article critically examines how various AI approaches detect early markers of fatigue, construct individualized injury prediction models, and support real-time coaching decisions. A systematic literature review was conducted on 20 selected scientific sources (2020–2026) from Scopus, PubMed, and Google Scholar databases. Findings indicate that AI-based systems achieve injury prediction accuracy of 85–92% in multimodal models, with injury incidence reductions of 22–30% in fully implementing institutions. In Indonesia, technology adoption remains at an early exploratory stage, constrained by infrastructure limitations, low data literacy among coaches, and absent athlete data privacy regulations. This article formulates strategic recommendations for accelerating the development of Indonesia's sports AI ecosystem.
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