<![CDATA[Purpose: These days there are often problems in the world sometimes have uncertain or vague answers. Therefore, fuzzy logic is one method for conducting such uncertain analysis. This thesis discusses the application of fuzzy logic Analysis of Prediction of Glove Production Quantities using the Sugeno method. The problem that is solved is to predict or predict the amount of production of goods because some workers in the company predict production figures by filling or the minds of the workers themselves based on the previous year's production output dataStudy method/design/approach: The first step for this study is to determine the input and output variables that are firm sets and then convert each variable into a fuzzy set consisting of Little, Medium, and Many by fuzzification process. It then processes the fuzzy set data through base rules defined by the minimum method to retrieve the smallest membership degree value previously calculated through the membership function representation. And the last one is the Sugeno Method Defuzzification, which is to find the value of the average weight centrallyResults/Findings: Based on prediction analysis calculations using Stock and production data from December 2018 to January 2023, the predicted amount obtained in the following year is higher than the actual production amount in the previous year. In January 2022, the actual production output obtained from PT. Medisafe Technologies amounted to 181,822,894 pcs, while the prediction results from calculations using the Sugeno fuzzy logic model amounted to 327,147,796 pcs. The error accuracy value using MAPE is 1.66%, which means that the accuracy of truth is 99.4%. So forecasting the amount of production using the Sugeno fuzzy logic model is very good for the company.Novelty / Originality / Value: The novelty of this study lies in the development of a model using the fuzzy sugeno method to predict the amount of glove production. This approach discusses to forecast the number of glove production in a company per month interval based on data on the amount of production in the previous year as an output variable and raw material inventory data as an input variable.]]>
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