@inproceedings{46a014ef34514c38b147a2a65674f36e,
title = "Indonesia's Food Commodity Price Forecasting using Recurrent Neural Networks",
abstract = "Managing strategic commodities prices in the market is considered an important task since they have a significant contribution to the calculation of the inflation rate. Inflation management has a strong connection to the public's economic activities and buying power. To aid this problem, it is necessary to find the best forecasting model that can predict commodities daily price. This paper aims to find the best prediction model between Recurrent Neural Network (RNN) variants, LSTM and GRU, in forecasting the daily price of three Indonesia's strategies commodities: rice, broiler meat, and chicken egg. The result shows that the GRU model achieves higher accuracy in predicting the daily price of rice, broiler meat, and chicken egg, based on two evaluation metrics Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The GRU model also managed to finish the computational process faster than LSTM by$\sim$20 seconds.",
keywords = "Forecasting, GRU, LSTM, strategic commodity",
author = "Savira Amalia and Arian Dhini and Zulkarnain",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022 ; Conference date: 23-06-2022 Through 25-06-2022",
year = "2022",
doi = "10.1109/IC3SIS54991.2022.9885249",
language = "English",
series = "Proceedings of International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022",
address = "United States",
}