<![CDATA[This study aims to design an egg appearance assessment system using fuzzy logic with input parameters of weight and sphericity. Manual egg sorting based on physical characteristics such as size and shape is often time-consuming and subjective, necessitating an automated classification system. The Mamdani fuzzy inference system was implemented using MATLAB Fuzzy Logic Toolbox to simulate the decision-making process. Triangular and trapezoidal membership functions were used for the input variables (weight and sphericity) and output variables (appearance quality), with linguistic terms such as light, normal, heavy, and abnormal, normal, round. The rule base consisted of nine if–then statements, with the defuzzification process using the centroid method. The system produced a crisp output value of 31.67, which falls into the “good” category, indicating that eggs weighing 55 g and with a roundness of 0.85 are classified as good quality eggs. Visualization through Surface Viewer and Rule Viewer shows that this model is capable of capturing nonlinear relationships between variables and providing adaptive classification results. The results of this study indicate that Mamdani fuzzy logic can assess egg quality accurately and efficiently, and serve as a reliable basis for the development of intelligent automatic sorting systems in the poultry industry.]]>
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