<![CDATA[This study is motivated by the importance of controlling the stability of strategic food prices to support food security and economic stability in Lubuklinggau City. Price fluctuations are influenced by the complexity of the supply chain, while conventional price reporting and food distribution systems result in delays in information dissemination and less effective policy decision-making. This study aims to develop E-Pangkal AI, a digital platform that integrates real-time food price and distribution monitoring, Long Short-Term Memory (LSTM)-based price forecasting, and local wisdom-based analysis to support regional price stabilization and inflation control. The research employed the Research and Development (R&D) method using the Prototype development model, which includes system design, prototype development, user evaluation, and iterative system refinement. The commodities analyzed consisted of rice, shallots, red chili peppers, cooking oil, and chicken meat, with a total of 1,797 datasets. The results of the LSTM model evaluation demonstrated good forecasting accuracy based on the Mean Absolute Percentage Error (MAPE), with values of 1.96% for rice, 6.98% for shallots, 3.63% for red chili peppers, 2.80% for cooking oil, and 2.06% for chicken meat. The findings indicate that the E-Pangkal AI platform is capable of integrating food price monitoring, food distribution monitoring, and LSTM-based price forecasting, enabling it to serve as a decision support system for the Government of Lubuklinggau City]]>
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