Performance Evaluation of Long Short-Term Memory for Chili Price Prediction
DOI:
https://doi.org/10.14421/jiska.2025.10.1.33-47Keywords:
LSTM, Prediction, RMSE, Chili Prices, GroceriesAbstract
Groceries prices often experience fluctuations in several regions in Indonesia, such as East Java Province and one of the commodities is chilies, both red chilies and rawit chilies. Predictive steps that utilize machine learning such as Long-Short Term Memory (LSTM) can be taken to estimate the next price of chili with expectations that the appropriate strategy can be taken by the authorities. LSTM is a network that developed from RNN networks in previous times by offering a longer cell memory so that more information can be stored. This research focuses on finding out whether the LSTM network can be applied to the case of chili price prediction and what architecture and hyperparameter configuration is appropriate for this case. For this reason, the experimental method is used by testing several predetermined variables to obtain the right architecture and hyperparameter configuration. The results of this research show that the LSTM network can be applied in this case and the architecture and best hyperparameter configuration obtained are the same for both types of chilies, namely red chilies and rawit chilies. For red chili, the best RMSE value that can be produced is 1751.890 and 1888.741 for rawit chili.
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