<![CDATA[Beef prices in the market tend to experience unpredictable changes influenced by various factors, such as consumer demand and feed costs. This instability can make it difficult for business actors and farmers to determine the right pricing strategy. This study aims to build a beef price prediction system using the multiple linear regression method with input variables in the form of the amount of demand and feed costs. Data were obtained from the National Food Price Panel and processed using a statistical approach to form a predictive model. The Root Mean Square Error (RMSE) value is used as an indicator of model accuracy. The model results are then implemented into a website-based application using the PHP programming language and MySQL database. This application allows users to enter input data and get real-time price prediction results. With this system, it is expected to help farmers, traders, and policy makers in making more accurate and efficient decisions based on historical data. This system can also be further developed by adding other related variables to increase the level of accuracy of beef price predictions.]]>
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