Penerapan generalized regression neural networks untuk memprediksi produksi padi terhadap perubahan iklim di Kabupaten Barito Kuala

Abstract

Rice as the main staple food for the people of Indonesia is a crop that is vulnerable to climate change. The data collection and forecasting of rice yields is needed to support policies related to food security. This study aims to forecast the production of rice in Barito Kuala District as the largest rice-producing district in South Kalimantan using climate data as input. The climatic data used is from Syamsudin Noor Meteorological Station, while the output data is rice production data from Badan Pusat Statistik of South Kalimantan Province. The method used to forecast rice production is Generalized Regression Neural Networks (GRNN). RMSE value of 0.29677 was obtained from the test results using smoothness parameter with a value of 1.