<![CDATA[Diseases in pig livestock are a major cause of economic losses for farmers, especially when treatment is delayed, leading to disease spread and increased mortality rates. In addition, high consultation costs and limited access to animal health service centers (puskeswan) often result in farmers performing manual diagnosis, which is inaccurate and may worsen the livestock’s condition. This study develops a web-based expert system for diagnosing pig diseases using the Mamdani fuzzy logic method as a solution to assist farmers in conducting independent and early diagnosis based on observable symptoms. The system is built using Python with the Flask framework, Bootstrap for the user interface, and Supabase as a cloud-based database. The diagnosis process consists of three main stages: symptom fuzzification, rule-based fuzzy inference, and defuzzification using the centroid method. Testing was conducted on 8 types of diseases and 45 symptoms using validated test data, resulting in an accuracy of 100%, with the system consistently producing diagnostic outcomes that match the symptom inputs. The system also includes an online consultation feature with experts, enhancing accessibility and effectiveness in identifying diseases in pig livestock.]]>
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