<![CDATA[Pasteurized milk is highly susceptible to spoilage due to its rich nutritional content and sensitivity to temperature fluctuations during storage. Conventional methods for detecting milk spoilage are often time-consuming and require laboratory testing. This research aims to develop an early detection model for pasteurized milk spoilage using the Sugeno fuzzy inference system based on storage temperature and pH parameters. The model applies two input variables, namely temperature and pH, and one output variable that classifies the milk condition into three categories: safe, warning, and spoiled. Data were obtained by storing pasteurized milk at different temperature conditions while monitoring pH changes over time. The Sugeno fuzzy model was implemented using MATLAB to process the data and generate numerical output representing spoilage risk levels. The results show that the Sugeno fuzzy inference system can effectively classify the milk condition with a prediction accuracy of 86.7 percent. The model indicates that higher storage temperatures and lower pH values significantly increase the risk of spoilage. Therefore, the Sugeno fuzzy logic model can be applied as an efficient, quantitative, and reliable method for real time quality monitoring and early detection of pasteurized milk spoilage.]]>
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