Application of the Certainty Factor and Forward Chaining Methods to a Goat Disease Expert System

Abstract

Goats are livestock that is financially very attractive to rural Indonesian. Efforts to solve problems related to goat farming are necessary. One of them is maintaining the health of the cattle by knowing how to cope with disease-stricken goats. Goat productivity will decrease if the treatment of the disease is sub-optimal. Goat diseases are very diverse, ranging from mild to severe. Breeders themselves can traditionally treat several diseases without the involvement of veterinarians or experts. However, a larger number of diseases need treatment with the help of experts. Expert systems are a potential solution to help farmers. It will automatically suggest decisions or conclusions in solving a problem. This study observes an expert system built using the Certainty Factor combined with Forward-Chaining. By combining the two methods, the information generated may discover the type of disease and suggest its management effectively with a high degree of certainty. The system can expectedly become a reference for goat breeders to consult about their goat livestock diseases. The knowledge base of the system uses 21 types of symptoms, eight types of diseases, and their solutions. The user does not need to input the belief value and the disbelief value that is usually input in the expert system. By involving the admin as a knowledge base processor, the correctness of the conveyed information maintains.