Comparison of backpropagation and ERNN methods in predicting corn production

Abstract

East Java is one of the producers of food crops in Indonesia. Some food crop commodities in East Java Province are corn, soybeans, peanuts, sweet potatoes, and cassava. These food crops have many benefits to make the demand for production increase. The uncertain amount of food crop production will be a problem for the Department of Agriculture and Food Security of East Java Province in determining a policy. To overcome this problem, a system is needed to predict the production of food crops in East Java. This study compares the Backpropagation algorithm and Elman Recurrent Neural Networks (ERNN). The data in this study were obtained from the Department of Agriculture and Food Security of East Java Province starting from 2007-2020 per quarter. The result of this research is that trial scenario 1 produces the best MSE value of 0.00000063 on the Backpropagation algorithm compared to ERNN which only gets an MSE value of 0.00000627. Trial scenario 2 produces the best MSE value, which is 0.000000003 in the Backpropagation algorithm with gradient descent momentum, this is also better when compared to ERNN which gets an MSE value of 0.00000407. It can be concluded that the best algorithm in this study is Backpropagation with gradient descent momentum because it produces MSE values ​​with good prediction results from all algorithms compared.