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

Benno Pramuditya; Ary Prabowo

doi:10.30591/jpit.v10i4.9459

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

Benno Pramuditya, Ary Prabowo

Abstract

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.

Keywords

Bulking; Decision Support System; Diet; Fuzzy Logic; Genetic Algorithm; Optimization

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References

Amin, M. A., & Nugroho, R. A. (2022). Rancang bangun sistem pakar penentuan status gizi menggunakan metode fuzzy logic. Jurnal Teknologi dan Sistem Informasi, 3(1), 12–19.

Anggraini, R. D., & Hidayatullah, R. (2022). Implementasi fuzzy logic dan algoritma genetika dalam sistem pakar diagnosis gizi. Jurnal Informatika Kesehatan, 6(2), 78–85.

Handayani, S., & Prasetyo, D. (2020). Penerapan fuzzy tsukamoto untuk rekomendasi pola makan sehat. Jurnal Sistem Informasi dan Komputerisasi Akuntansi, 8(3), 45–53.

Hastuti, L., & Putri, M. D. (2021). Optimasi sistem pendukung keputusan menggunakan algoritma genetika. Jurnal Teknologi Informasi*, 5(1), 33–40.

Fauzi, A., & Subarkah, S. (2022). Sistem pendukung keputusan pemilihan diet menggunakan fuzzy logic berbasis website. Jurnal Ilmiah Teknik Informatika, 12(1), 51–58.

Saputra, R. A., & Wibowo, A. (2020). Penggunaan metode fuzzy logic untuk menentukan pola makan pada remaja. Jurnal Informatika dan Komputer, 15(2), 73–80.

Zahra, F., & Nurcahyo, A. (2021). Sistem cerdas rekomendasi program diet menggunakan logika fuzzy dan web. Jurnal Teknologi Informasi dan Terapan, 4(1), 19–26.

Hidayat, T. F., & Astuti, M. (2023). Sistem pendukung keputusan menggunakan fuzzy logic untuk menentukan status gizi anak. Jurnal Teknologi dan Sains, 7(2), 55–62.

Santosa, R., & Wulandari, D. (2020). Sistem pendukung keputusan dengan metode fuzzy untuk penentuan status gizi balita. Jurnal Ilmu Komputer dan Aplikasinya, 9(1), 13–19.

Wijaya, I., & Ramdhan, A. (2021). Pengembangan sistem pendukung keputusan dengan logika fuzzy dalam pemilihan pola diet. Jurnal Informatika dan Sistem Informasi, 6(2), 88–95.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

Herrera, F. (2008). Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence, 1, 27–46. https://doi.org/10.1007/s12065-008-0001-5

Runkler, T. A. (2012). Data Analytics: Models and Algorithms for Intelligent Data Analysis. Springer. https://doi.org/10.1007/978-3-642-28430-0

Gonzalez, C. I., & Perez, M. D. (2019). Personalized diet recommendation using fuzzy logic and genetic algorithm. Journal of Healthcare Engineering, 2019, 1–10. https://doi.org/10.1155/2019/9153125

Sadeghi, A., & Sharafat, A. R. (2021). A novel hybrid fuzzy–genetic approach to dietary recommendation system for obese individuals. Expert Systems with Applications, 174, 114679. https://doi.org/10.1016/j.eswa.2021.114679

DOI: https://doi.org/10.30591/jpit.v10i4.9459