The Application of Expert System for Diagnosing Diseases in Corn Plants Using Forward Chaining Method

Abstract

Corn is one of the most important food crops in Indonesia, but it is highly susceptible to various diseases that can significantly reduce yield. Diagnosing corn diseases typically requires agricultural experts, whose availability is often limited in rural areas. This research aims to develop an expert system to diagnose corn diseases using the Forward Chaining method. The system was built using PHP and MySQL with the CodeIgniter framework. The methodology included requirement analysis, system design using Unified Modeling Language (UML), implementation, and testing. The knowledge base was obtained from agricultural experts and existing literature, consisting of symptoms, diseases, and their corresponding solutions. Forward Chaining was applied to match user-selected symptoms with predefined rules to produce a diagnosis. The test results indicate that the system can accurately diagnose diseases based on the rules, helping farmers identify problems early and providing recommended solutions without requiring direct expert assistance. This study demonstrates the effectiveness of expert systems in agricultural disease management and offers a potential solution for farmers facing limited access to agricultural experts.