Optimasi Fuzzy Logic Menggunakan Genetic Algorithm (GA) dalam Menentukan Program Diet dan Bulking

The increasing demand for accurate and personalized diet and bulking programs highlights the need for a reliable decision support system (DSS). This study aims to develop a fuzzy logic–based DSS optimized with a Genetic Algorithm (GA) to recommend diet, bulking, or maintenance programs tailored to individual conditions. The methodology involved designing fuzzy sets, formulating IF–THEN rules, applying the Mamdani inference method, and optimizing fuzzy parameters using GA. Data were collected from 50 adult respondents, and the system was tested using 10 input scenarios validated by fitness experts. The results revealed that the fuzzy system without GA achieved only 38% agreement with expert recommendations, whereas GA optimization significantly improved accuracy to 82%. Furthermore, GA refined membership functions and eliminated irrelevant rules, producing a more streamlined yet precise system. The web-based interface facilitated user interaction and interpretation of results, ensuring practical usability. In conclusion, integrating fuzzy logic with GA enhanced the accuracy and adaptability of the system for determining diet and bulking programs, establishing it as a promising decision-making tool that can be further expanded with additional personalization variables in the future.