<![CDATA[Papaya is one of the most widely cultivated horticultural commodities in Indonesia. However, this plant is susceptible to various diseases that can reduce productivity and fruit quality. Limited knowledge among farmers in identifying early disease symptoms is a major factor in failed disease management. This research aims to develop an expert system using the Mamdani Fuzzy Logic method to assist in determining types of diseases in papaya plants. The system utilizes fuzzy inference techniques capable of handling uncertain and linguistic data, and is built using the Python programming language and MySQL database. Diagnosis is performed based on symptoms entered by users, processed through fuzzification, inference, and defuzzification stages to produce the most probable disease type and its certainty level. The test results show that the system provides accurate diagnoses that align with manual expert analysis using predefined fuzzy rules. Therefore, the expert system serves as an effective solution for supporting fast and accurate identification of diseases in papaya plants.]]>
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