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Submitted
17 October 2025
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17 November 2025
Published
29 November 2025
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Abstract

The risk of injury in weightlifting, particularly in the deadlift movement, is often caused by subjective initial load determination and exacerbated by the egolifting phenomenon. To mitigate this risk, this research proposes an intelligent hybrid model for personalized load recommendation. This model integrates a Fuzzy Inference System (FIS) with a Genetic Algorithm (GA) to perform end-to-end parameter optimization. The fuzzy system utilizes four inputs (BMI, WHtR, RHR, Experience) to represent the user's condition. The Genetic Algorithm then automatically tunes 18 crucial system parameters, including membership functions and adjustment factors, using 20 real data points from an expert as the ground truth. The research results show that optimization using GA successfully reduced the Mean Absolute Error (MAE) significantly. The validated final model achieved an accuracy of 74.78% and an MAE of 6.37 kg, confirming that the hybrid Fuzzy-Genetic approach is a superior method for tuning quantitative recommendation systems, resulting in more precise and reliable decisions.

Keywords

Fuzzy Inference System Genetic Algorithm Optimization Load Recommendation Deadlift

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Copyright (c) 2025 Jurnal Komputer Terapan

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Author Biography

Arief Hermawan, Universitas Teknologi Yogyakarta

Prodi Informatika Fakultas Sains dan Teknologi Universitas Teknologi Yogyakarta
How to Cite
Naufal, A. R., & Hermawan, A. (2025). OPTIMIZATION OF FUZZY INFERENCE SYSTEM USING GENETIC ALGORITHM FOR PERSONALIZED INITIAL DEADLIFT LOAD RECOMMENDATION. Jurnal Komputer Terapan, 11(2), 77–87. https://doi.org/10.35143/jkt.v11i2.6780
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