Pendekatan LSTM Berbasis Deep Learning dalam Memprediksi Fluktuasi Harga Cabai
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Aryka Anisa Pertiwi
*
Universitas Logistik dan Bisnis Internasional, Bandung,
Indonesia
- Nisa Hanum Harani Universitas Logistik dan Bisnis Internasional, Bandung, Indonesia
- Cahyo Prianto Universitas Logistik dan Bisnis Internasional, Bandung, Indonesia (*) Corresponding Author
Abstract
The significant fluctuation in chili prices in Indonesia leads to economic instability, particularly for consumers and market stakeholders. This study aims to develop a daily chili price prediction model using the Long Short-Term Memory (LSTM) algorithm based on deep learning, designed to capture seasonal patterns and long-term dependencies in historical data. The research adopts the CRISP-DM approach, encompassing business understanding, data processing, model training, and implementation into a web-based dashboard. The dataset, collected from Pagar Alam City between 2022 and 2024, includes features such as previous prices, chili sub-variants, sinusoidal time transformations, and market conditions. The LSTM regression model demonstrated high performance, achieving an R² score of 0.9567, a MAE of 1,402.92, and an RMSE of 2,595.98. Additionally, a classification model was developed to predict price status (increase, decrease, stable) as a decision-support tool. The deployment of this system into an interactive dashboard enables real-time price predictions. These results indicate that the LSTM-based approach is not only technically accurate but also offers a practical solution for commodity price monitoring and decision-making in the food sector.
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