<![CDATA[In Indonesia, Oryza sativa is a major food commodity, and disease attacks, particularly those caused by fungal infections, greatly affect its productivity. Fungal infections in rice plants are often difficult to detect early due to the similarity of visual symptoms among different types of diseases and the limited knowledge of farmers. This research aims to implement an expert system that uses the forward chaining method to detect fungal attacks on rice plants early, based on visual symptoms. The forward chaining method is used as an inference mechanism by utilizing IF–THEN rules compiled based on interviews with agricultural experts and literature studies. The expert system was developed in the form of a web-based application and designed using UML modeling. The data used includes visual symptoms, types of fungi, and control methods. System testing is conducted using the black-box testing method to ensure functionality and diagnostic accuracy. The research results indicate that the system is capable of identifying the type of fungus on rice plants well based on the given symptoms and producing appropriate control recommendations. Thus, this expert system is expected to become an effective tool for farmers in detecting and handling fungal attacks more quickly and accurately.]]>
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