Peramalan Harga Cabai Rawit Merah Menggunakan Attention Mechanism Berbasis Long Short-Term Memory
Red cayenne pepper is a commodity that has important economic value in Indonesia, especially in West Java Province. Cayenne pepper often experiences significant price fluctuations which can cause inflation. In 2022 there will be the highest inflation in West Java in the last eight years due to incr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Indonesian Society of Applied Science (ISAS)
2024-12-01
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| Series: | Journal of Applied Computer Science and Technology |
| Subjects: | |
| Online Access: | https://journal.isas.or.id/index.php/JACOST/article/view/875 |
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| Summary: | Red cayenne pepper is a commodity that has important economic value in Indonesia, especially in West Java Province. Cayenne pepper often experiences significant price fluctuations which can cause inflation. In 2022 there will be the highest inflation in West Java in the last eight years due to increased commodity prices, including cayenne pepper. There needs to be an effort to maintain the stability of the price of red cayenne pepper in West Java. This research aims to create a price forecasting system for red cayenne peppers in West Java by comparing two deep learning approaches, namely Long-Short Term Memory (LSTM) and Long-Short Term Memory with Attention Mechanism (LSTM-Attention-LSTM) to obtain high accuracy in predicting the price of red cayenne pepper. The results of this research show that the LSTM model using 3 hidden layers, 100 neurons, 128 dense, 1 dense, and 32 batch sizes, produces Mean Absolute Error (MAE) values of 0.023, Root Mean Square Error (RMSE) of 0.152, and Mean Absolute Percentage Error (MAPE) is 3.68%. Meanwhile, the LSTM-Attention-LSTM model with the same configuration produces an MAE value of 0.017, RMSE of 0.130, and MAPE of 2.74%. The results of this research can be a reference for the community and government in maintaining price stability for cayenne pepper in West Java.
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| ISSN: | 2723-1453 |