2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 1 , PP: 183-195, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Manshuralhudlori 1 * , Agus Kristiyanto 2 , Rony Syaifullah 3 , Febriani Fajar Ekawati 4 , Slamet Riyadi 5 , Fadilah Umar 6
Doi: https://doi.org/10.54216/FPA.170113
This study presents a novel approach to predictive modeling of muscular performance and fitness progression using artificial intelligence techniques. Leveraging advanced machine learning algorithms, including artificial neural networks (ANN), support vector machines (SVM), and gradient boosting machines (GBM), we develop a comprehensive model capable of accurately forecasting key metrics related to muscular strength, endurance, and overall fitness. Extensive experimentation and evaluation demonstrate the superiority of the proposed method over existing algorithms across a range of performance metrics, including accuracy, precision, recall, F1-score, and error metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Our findings highlight the importance of feature selection techniques and model hyperparameter optimization in driving predictive performance, underscoring the need for careful model development and tuning. The practical implications of our research extend to sports science and athletic training, where the proposed method can inform personalized training strategies tailored to individual athletes' needs and goals. Moving forward, further research is needed to validate the robustness and generalizability of the proposed method across different populations and athletic disciplines, as well as to explore its integration with real-time data sources for more dynamic and responsive training programs.
Artificial Intelligence , Athletic Training , Fitness Progression , Machine Learning , Muscular Performance , Predictive Modeling , Sports Science , Training Strategies , Workout Optimization , Wearable Technology
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