Prediksi Padi Menggunakan Algoritma Long Short Term Memory
DOI:
https://doi.org/10.47065/jimat.v5i2.496Keywords:
Forecasting; Rice; Long Short Term Memory; Mean Absolute Percentage Error; Time SeriesAbstract
Rice is one of the main agricultural commodities in Indonesia, including in Lubuklinggau City, which is a rice-producing area in South Sumatra Province. However, rice production fluctuates every month due to various factors such as planting seasons, land conversion, weather, and pest attacks. This instability can affect food availability and farmer welfare. Therefore, rice production forecasting is important in supporting better decision-making in the agricultural sector. This study uses monthly rice production data from January 2019 to November 2024 obtained from the Lubuklinggau City Agriculture Service. The method used is Long Short-Term Memory (LSTM), which is one of the artificial neural network techniques based on time series data. The optimal parameters used in the model are the number of neurons in the hidden layer of 35, a batch size of 12, and a maximum of 50 epochs. The results showed that the model with optimal parameters produced a Mean Absolute Percentage Error (MAPE) value of 4.44%, which is included in the very good category. These results indicate that the LSTM method can be used effectively to predict rice production in Lubuklinggau City with a high level of accuracy.
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