<![CDATA[Shallot cultivation faces challenges from various pests and diseases that can affect crop growth and productivity. Identifying these problems based on observable symptoms is important for determining appropriate management measures. This study aims to develop a web-based expert system for identifying pests and diseases in shallot plants using the forward chaining method. The system was developed using PHP and MySQL, with a knowledge base comprising pest and disease categories, symptoms, diagnostic rules, and recommended management measures. The inference process applies IF–THEN rules to match user-selected symptoms with the conditions represented in the knowledge base and generate diagnostic results. System validity was evaluated by comparing the system's diagnostic outputs with diagnoses provided by an agricultural expert across nine test cases. The evaluation table reports agreement between the system and expert diagnoses in all nine cases, although some cases produce multiple candidate diagnoses with different matching percentages. These findings indicate that the system can support preliminary identification of shallot pests and diseases based on the implemented knowledge base. Further evaluation using a larger and more diverse set of test cases is needed to establish diagnostic accuracy and assess the system's reliability. The system is intended to support farmers in recognizing potential crop problems and obtaining relevant management information.]]>
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