<![CDATA[Abstract. Melon plants are one of the horticultural commodities that have high economic value. However, in the cultivation process, melon plants often experience various disease attacks that can reduce the quality and yield of the harvest. Farmers often have difficulty recognizing disease symptoms at an early stage due to limited knowledge and lack of access to agricultural experts. Therefore, a system is needed that can assist the process of diagnosing plant diseases quickly and accurately. This study aims to design and develop a web-based expert system that can be used to diagnose diseases in melon plants using the Naïve Bayes method. The knowledge base of this system consists of 21 symptoms and 6 types of melon plant diseases obtained from various references and related literature. The diagnosis process is carried out by calculating the probability value of each disease based on the symptoms selected by the user using the Naïve Bayes method. The system then determines the disease with the highest probability value as the diagnosis result. The results of system testing show an accuracy level of 92%, indicating that the Naïve Bayes method is able to classify most of the test data correctly. Therefore, the developed system can assist farmers in identifying melon plant diseases more quickly so that appropriate handling can be carried out effectively.]]>
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