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All Journal Jurnal Komputer Terapan
Naufal, Atha Redian
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Computer Science & IT

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OPTIMASI SISTEM INFERENSI FUZZY DENGAN ALGORITMA GENETIKA UNTUK PERSONALISASI REKOMENDASI BEBAN AWAL LATIHAN DEADLIFT Naufal, Atha Redian; Hermawan, Arief
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6780

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.
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