Prediksi Harga Bawang Merah Menggunakan Gated Recurrent Unit (GRU)
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1374Keywords:
Gated Recurrent Unit; Shallots; Price Prediction; Time Series; Univariate Time SeriesAbstract
Shallots are one of the horticultural commodities with high economic value and play an important role in food supply in Indonesia. Fluctuations in shallot prices in East Java Province highlight the need for forecasting methods to estimate prices in future periods. This study applies the Gated Recurrent Unit (GRU) method to predict shallot prices using a univariate time series approach. The data consist of daily shallot prices obtained from the Food Staple Availability and Price Development Information System (SISKAPERBAPO) of East Java Province, covering the period from January 1, 2015, to March 31, 2026. After data cleaning, a total of 4,108 observations were obtained. The research stages include data exploration, data cleaning, normalization using Min-Max Scaling, sequence formation, model training, and performance evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experiments used an 80:10:10 ratio for training, validation, and testing data, respectively, with variations in sequence length, the number of hidden units, and the number of epochs. The best configuration was selected based on the lowest MAPE value on the test data, while also considering MAE and RMSE. The experimental results identified the best configuration as a sequence length of 30 days, 64 hidden units, 50 epochs, and a batch size of 32, yielding an MAE of IDR 270.26, an RMSE of IDR 423.46, and a MAPE of 0.7073%. The model with this configuration was subsequently used to perform three-day recursive forecasting. The forecasting evaluation yielded an MAE of IDR 518.39, an RMSE of IDR 538.34, and a MAPE of 1.43%. These results indicate that the GRU model can produce shallot price predictions with relatively low error levels under the evaluated conditions. However, forecasting performance remains dependent on historical data patterns and the model configuration used; therefore, the predictions should be interpreted with consideration of these limitations.
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