<![CDATA[Curly red chili is a strategic national commodity characterized by extreme price fluctuations, which significantly impact regional inflation and farmer welfare. Although conventional statistical methods are frequently used for forecasting, these approaches have inherent limitations in capturing non-linear volatility and dynamic price patterns. This research aims to address this gap by comprehensively comparing the performance of the AutoRegressive Integrated Moving Average (ARIMA) statistical model and the Long Short-Term Memory (LSTM) Deep Learning model. This study utilizes a univariate prediction approach based on daily historical price data from January 2024 to October 2025. The dataset is partitioned into 80% for training and 20% for testing purposes. Model performance is rigorously evaluated using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (). The experimental results demonstrate that the LSTM model significantly outperforms ARIMA in tracking daily price trends. LSTM achieved an average MAPE of 13.76% (classified as "Good") with an value of 0.92, whereas the ARIMA model recorded a significantly higher MAPE of 41.21% and a negative value. This study concludes that Deep Learning-based algorithms are superior and more effective in handling food commodity price volatility compared to classical linear statistical methods.]]>
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