Fuzzy Logic Model for Evaluating Fresh Milk Quality Based on Protein to Fat Ratio - Mula Gabe Ompusunggu; Gabby Livia Satria Gani, Gusti Ayu Putu Shintya Saraswati, Helmayanti Pratikasari, Mohamad Jalal Fah (Journal of Applied Science, Technology & Humanities | JASTH, 2026)
The assessment of fresh milk quality plays a crucial role in ensuring food safety and supporting the efficiency of dairy processing. This study aims to develop a fuzzy logic model to evaluate the acceptability of fresh milk based on the protein-to-fat ratio in accordance with the Indonesian National Standard (SNI 3141.1:2011). A qualitative descriptive method was applied using MATLAB software to simulate fuzzy inference with protein and fat contents as input variables and milk quality as the output. Triangular and trapezoidal membership functions were utilized, with the Mamdani inference and centroid defuzzification methods used to obtain precise output values. The results indicate that samples with high and balanced protein and fat contents yield superior milk quality, whereas low levels of both result in poor classification. The fuzzy rule system effectively accommodates natural variations in milk composition, offering a more adaptive and accurate assessment compared to conventional threshold-based evaluations. This model demonstrates strong potential as a decision-support tool for quality control in the dairy industry and contributes to improving the consistency and scientific basis of national milk quality assurance systems.