Cat diseases are one of the most common health problems faced by pet owners. However, limited knowledge of disease symptoms often leads to delays in diagnosis and proper treatment. This study aims to design and develop an Expert System for Diagnosing Cat Diseases Using the Fuzzy Logic Method to assist users in identifying diseases based on the symptoms experienced by cats. The system was developed as a web-based application by utilizing a knowledge base obtained from veterinary experts and a rule-based inference system that represents the relationship between symptoms and diseases. The method employed in this research is Fuzzy Logic, consisting of the stages of fuzzification, rule-based inference, and defuzzification to determine the probability level of each disease. The system incorporates 33 symptoms and 7 types of cat diseases, namely Scabies, Dermatitis, Otitis (Ear Inflammation), Poisoning, Eye Inflammation, Worm Infestation, and Rabies. Each disease is modeled using fuzzy membership sets categorized into low, medium, and high severity, enabling the system to accommodate uncertainty during the diagnostic process. The testing results demonstrate that the expert system is capable of generating membership values and probability scores for each disease based on the symptoms selected by the user. In the case study, the system identified Dermatitis as the most probable disease with a score of 0.90, followed by Scabies with a score of 0.592, while the remaining diseases showed relatively low probability values. These findings indicate that the Fuzzy Logic method is effective as a decision-making approach in expert systems, as it provides a more flexible representation of diagnostic confidence compared to binary logic.
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