<![CDATA[Rice is a strategic food commodity in Indonesia due to its role as the main staple food and its significant contribution to inflation and economic stability. Fluctuations in rice prices directly affect purchasing power and are often used as an important indicator in assessing macroeconomic conditions. At the regional level, South Sulawesi plays a crucial role as one of the national food barns, where price dynamics may influence food availability and distribution, particularly in Eastern Indonesia. However, rice price data often exhibit non-linear patterns and sudden fluctuations, making accurate forecasting a challenging task. This study aims to evaluate the performance of the Neural Network Autoregression (NNAR) model in forecasting monthly rice prices in South Sulawesi. The study uses secondary time series data consisting of 61 observations from January 2021 to January 2026. The NNAR model is applied to capture non-linear relationships using lag-based inputs within a feed-forward neural network framework. The model performance is evaluated using Mean Absolute Percentage Error (MAPE) under several data splitting scenarios. The results show that the best model is NNAR (1,3) with a data split of 80% training and 20% testing, producing a MAPE value of 3.572%, which indicates excellent forecasting ability. The forecasting results suggest that rice prices are expected to remain relatively stable with a slight downward trend in the upcoming period. Overall, the NNAR model demonstrates strong capability in capturing the underlying patterns of rice price data and provides reliable forecasting performance. This study contributes to the development of time series forecasting methods and provides useful insights for policymakers in managing food price stability.]]>
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