Hybrid Fuzzy Logic, Genetic Algorithms, and Artificial Neural Networks for Cattle Body Weight Prediction

Authors

  • Anjar Setiawan Universitas Pamulang
  • Ema Utami Universitas Amikom Yogyakarta
  • Dhani Ariatmanto Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.55606/juisik.v5i2.1319

Keywords:

Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Hybrid Algorithm, Mean Squared Error

Abstract

Cattle serve as the primary means of meat and milk production in numerous regions across the globe. Enhancing efficiency and productivity in cow ranching can provide significant economic consequences. The cattle industry is significant as it enables the estimation of cow weight, directly influencing beef and milk quality. This study aims to enhance the accuracy of cattle weight estimation by minimizing the Mean Squared Error (MSE) values. The integration of artificial neural network (ANN), fuzzy logic (FL), and genetic algorithm (GA) techniques is a promising artificial intelligence tool for predicting and modeling cattle weight in livestock weight prediction systems. The cow weight forecast yielded a Mean Squared Error (MSE) value of 10.9 kg, which is the best result. The results demonstrate the progress made in agriculture using advanced technologies. They offer a detailed examination of how artificial intelligence, fuzzy logic, and evolutionary techniques can be combined to address the many difficulties associated with estimating cattle body weight.

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Published

2025-07-01

How to Cite

Anjar Setiawan, Ema Utami, & Dhani Ariatmanto. (2025). Hybrid Fuzzy Logic, Genetic Algorithms, and Artificial Neural Networks for Cattle Body Weight Prediction. Jurnal Ilmiah Sistem Informasi Dan Ilmu Komputer, 5(2), 317–332. https://doi.org/10.55606/juisik.v5i2.1319

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