Pendekatan LSTM Berbasis Deep Learning dalam Memprediksi Fluktuasi Harga Cabai - Pertiwi, Aryka Anisa; Harani, Nisa Hanum; Prianto, Cahyo (Building of Informatics, Technology and Science (BITS), 2025)
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.