Expert System Design for Laying Hen Disease Diagnosis Using Forward Chaining and Certainty Factor

Abstrak

Laying hens are a crucial part of the poultry industry, significantly contributing to the fulfillment of animal protein needs. However, the prevalence of various diseases remains a primary threat that causes a drastic decline in egg production and high mortality rates. The delay in diagnosis, exacerbated by the limited availability of veterinary specialists in rural areas, emphasizes the need for an accessible diagnostic tool. This research aims to develop a web-based expert system capable of diagnosing 11 types of laying hen diseases based on 27 observable symptoms. The system implements the Forward Chaining method as an inference engine to systematically trace disease rules starting from the user-inputted symptoms. Furthermore, the Certainty Factor (CF) method is integrated to calculate the confidence level of the diagnosis, utilizing the Measure of Belief (MB) and Measure of Disbelief (MD) values provided by an expert veterinarian. The system was tested using Blackbox testing for functional validation, which yielded 100% functionality. Furthermore, accuracy testing across 18 real-world test cases demonstrated that the system achieved an accuracy rate of 77.78%, with 14 cases perfectly matching the expert's diagnosis. This expert system proves to be highly effective in assisting farmers in performing early disease identification and taking timely preventive measures.