Expert System for Diagnosing Diseases in Corn Plants using Forward Chaining and Certainty Methods Web-Based Factor

Abstract

Corn is one of the main agricultural communities that plays an important role in supporting community food security. In Ndapayami Village, East Sumba Regency, corn farming is the main source of livelihood for the community. However, the productivity of corn crops often decreases due to disease attacks that are difficult for farmers to identify quickly. This study aims to design and build an expert system in diagnosing diseases in corn plants using the Forward Chaining method as an inference engine and Certainty Factor to measure the level of certainty of diagnosis. The expert system is designed to be computer-based so that it is easily accessible to farmers and agricultural extension workers in determining solutions to handle corn crop diseases. The system was developed using a rule-based approach and tested through the black-box testing method in 15 case scenarios. The research data was obtained through field observations, interviews, and documentation, and validated by agricultural experts. The test results show that the expert system has an accuracy rate of 80% and is able to give a confidence value to each diagnosis result. This system is expected to help speed up the disease diagnosis process, increase farmers' knowledge, and support increased productivity and food security in Ndapayami Village.