Chili Price Prediction One Year Ahead Using the Gated Recurrent Unit Method

Abstract

Chili is an important commodity in the Indonesian economy, and it experiences significant price fluctuations. This is due to several factors, such as demand and supply in the market and climate and temperature factors. The prediction of chili prices is important to minimize the risk of loss to the community and the government. Previous studies have used ARIMA, SVR, MLP, and RNN methods to predict chili prices. However, these methods are still considered less effective, especially in processing large amounts of information. In this study, the Gated Recurrent Unit (GRU) method is used as an alternative, and this method is designed to overcome obstacles to remembering long information. The results show that GRU provides excellent prediction performance, with a MAPE value below 10% for all types of chili peppers. The test results that have been carried out show that the size of the window data affects the accuracy of predicting chili prices. In addition, the combination of hyperparameters, namely hidden neurons and epochs, also influences the accuracy of predicting chili prices. The lowest MAPE value is obtained in predicting large red chili pepper prices of 4.850% with 512 hidden neurons and 100 epochs. Then, a MAPE of 6.434% was obtained using 512 hidden neurons and 100 epochs to predict curly red chili pepper prices. In predicting the price of green bird's eye pepper, a MAPE of 7.288% is obtained with a combination of 512 hidden neurons and 150 epochs. Finally, the lowest MAPE value is obtained in predicting the price of red bird's eye pepper at 6.452% with a combination of 512 hidden neurons and 150 epochs.