<![CDATA[Fluctuations in the price of Fresh Fruit Bunches (FFB) of palm oil remain a problem in the pricing process because they are influenced by several dynamically changing economic factors. This study aims to apply the Mamdani Fuzzy Logic method to predict FFB prices by considering data uncertainty and relationships between variables that are not always linear. The variables used include the K Index, Crude Palm Oil (CPO) price, CPO ratio (R-CPO), palm kernel price (IS), and palm kernel ratio (R-IS). All variables are processed in a rule-based fuzzy inference system using MATLAB, then validated through a comparison between manual calculations and simulation results. The scientific contribution of this study lies in the application of a FFB price prediction system that integrates several key economic indicators in one Mamdani Fuzzy model, as well as the use of double validation to ensure the consistency of the calculation process. The test results show a Mean Absolute Percentage Error (MAPE) value of 18.7%, which is included in the good category and indicates that the model is able to follow the actual data pattern with an acceptable error rate. The comparison of the manual calculation result of 2499.13 and the MATLAB result of 2570 shows a relatively small difference, thus supporting the consistency of the method used. With its rule-based and easily traceable characteristics, the Fuzzy Mamdani method can be the basis for an interpretive and applicable decision support system to help predict FFB prices in the palm oil plantation sector.]]>
