Export Commodity Price Forecasting in Indonesia Using Decision Tree, Random Forest, and Long Short-Term Memory

Abstract

Gross Domestic Product (GDP) is an indicator that becomes a benchmark for a country's economic performance. One of the factors that significantly affect GDP is export activity. However, the problem that occurs is that the export value is relatively fluctuating, this is because commodity prices are always changing every time. Therefore, we need a system that can predict commodity prices accurately. It is hoped that this system can help the government to make appropriate export policies based on predictions of commodity prices in the future. The contribution of this study is to compare Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) performance in forecasting several export commodities in Indonesia. In this study, the commodities forecasted are the main commodities from each sector that dominates exports in Indonesia, namely palm oil from the manufacturing sector, coffee from the agricultural sector, and coal from the mining sector. The experiments in this study were conducted by testing several hyperparameters of each method to determine the best model. The performance of models is measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that LSTM has the lowest error among Decision Tree and Random Forest with MAPE of 0.121, 0.494, and 0.282 in forecasting coal, coffee, and palm oil price respectively. Therefore, LSTM has proven to be the best method among Random Forest and Decision Tree in forecasting export commodity prices in Indonesia.