Comparative Analysis of Large Red Chili Price Forecasting Models in Malang Regency Using Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) - Indrawan, Yogi Fradika; Larasati, Aisyah; Purnama, Agus Rachmad; Sholikha, Nikmatus (Intelligent System and Computation, 2025)
Large red chili is a strategic food commodity with high demand, yet its price often fluctuates due to factors such as weather, harvest seasons, and market dynamics. In Malang Regency, these fluctuations impact inflation and economic stability, necessitating an accurate forecasting model to support decision-making. This study aims to develop a price forecasting model using Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods and compare their performance using daily time series data on large red chili prices from January 2022 to August 2024, obtained from the Representative Office of Bank Indonesia in Malang. The data underwent preprocessing, where LSTM data was transformed using MinMaxScaler, while ARIMA data was differenced to meet stationarity assumptions, then split into 80% training and 20% testing data, with optimal parameters obtained through Grid Search for both models. The results show that the LSTM model with three layers (150, 150, 150 units) and a dropout of 0.2 achieved an RMSE of 2.326 and MAPE of 3.65%, whereas the best ARIMA configuration (4,1,3) achieved an RMSE of 2.455 and MAPE of 3.80%. Although both models performed competitively and yielded promising results, LSTM demonstrated superior accuracy in forecasting large red chili prices in dynamic market conditions.