<![CDATA[This study aims to predict the “best’ amount of production so that there is no shortage of inventory or excess inventory. Fluctuations in the number of requests for crispy caused uncertainty about the amount of inventory and the amount of production. The Mamdani fuzzy method as a fuzzy logic inference engine can be used to overcome this uncertainty. The steps taken are to determine the inference system input, fuzzification, implication, aggregation and defuzzification. The set data used is supply (X) and demand (Y) for crispy pempek in December 2022. Furthermore, this data is mapped into the fuzzy membership function values using ascending function, decreasing function and triangular function graphs. At this stage, the fuzzy membership function equations are determined in 3 linguistic variables, each supply and demand input data will be mapped into a few, medium or many sets. The provisions that apply are if the fuzzy membership values are added up, it will produce a value of 1. Furthermore, at the implication, 9 basic rules are determined in IF-THEN rule. AT the aggregation stage use the max min function, where the maximum membership function value will be selected for supply and demand variables. In test data 1, the amount of inventory is included in the medium category with a fuzzy membership function value of 0,914 and the number of demand is included in the medium category with a fuzzy membership function value of 0,860 so that the membership function for the variables number of production is min {0.914, 0.860} = 0.860. The predicted ‘best’ production number in January are 306(LOM) and 271 (MOM). The Mamdani method can be applied to determine forecasting the amount of production so that the amount of inventory is as needed.]]>
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