<![CDATA[Corn is one of the leading food commodities in Indonesia, and it has a vital role in national food security. However, corn productivity often decreases due to the attacks of various types of plant diseases. Limited knowledge of farmers about the symptoms and types of corn diseases is one of the obstacles to proper and fast handling. Therefore, this study aims to build an expert system that can assist farmers in diagnosing corn diseases based on the symptoms shown. This system uses the Backward Chaining method, which tracks logic from conclusions (disease hypotheses) to the facts (symptoms) that support it. The tracing process is carried out until the appropriate facts are found to decide on the diagnosis of the disease. The system design is carried out using the prototype system development method. The testing is carried out by the Black Box Testing method to ensure that all system functions run as expected. The test results show that this expert system can provide accurate diagnoses, assist users in recognizing the type of corn disease, and provide treatment advice. Hopefully, this system can increase the effectiveness of handling corn diseases and support crop productivity.]]>
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