Implementasi Recurrent Neural Network GRU Dalam Menentukan Harga Ayam Di Jawa Timur - Zainuddin, Mohammad; Wahyu Ramadhan, Dony (JURNAL SISTEM KOMPUTER ASIA, 2024)
Significant price fluctuations pose a serious challenge for breeders, traders and consumers. This price uncertainty has an impact on the economic stability of breeders and public access to protein sources. This problem also affects the sustainability of the chicken farming industry in East Java. In response to the problem of chicken price fluctuations, this research applies the Gated Recurrent Unit (GRU) method to predict future chicken prices. The process begins with collecting a historical dataset of chicken prices from the East Java region. After that, the GRU model training process is carried out using this data to increase prediction accuracy. Model evaluation is carried out using metrics such as RMSE, MAPE, and Accuracy to ensure model validity and performance. The research results show that the GRU model provides chicken price predictions with a satisfactory level of accuracy of 99.72%. Evaluation metrics such as RMSE and MAPE show that the model is able to provide predictions that are close to actual values, while the accuracy level of the model also reflects its ability to provide good estimates.