Expert System for Detecting Diseases of Potatoes of Granola Varieties Using Certainty Factor Method - Bonifacius Vicky Indriyono; Moch. Sjamsul Hidajat; Tri Esti Rahayuningtyas; Zudha Pratama; Iffah Irdinawati; Evita Citra Yustiqomah (International Journal of Artificial Intelligence & Robotics (IJAIR), 2022)
The low productivity of potatoes is caused by many factors, including the very low quality of the seeds used, poor storage, climate, capital, limited farmer knowledge, and attacks by plant-disturbing organisms, especially diseases. Not only that, many farmers are still unfamiliar with the various diseases that can attack potato plants, or their knowledge about potato plant diseases is incomplete. This study aims to design and develop an expert system web-based application technology using the Certainty Factor (CF) method to detect potato disease symptoms. The CF method defines a measure of the capacity of a fact or provision to express the level of an expert's belief in a matter experienced by the concept of belief or trust and distrust or uncertainty contained in the certainty factor. The results showed that the CF method could function optimally in detecting potato plant diseases which can help farmers based on the symptoms that appear with an accuracy value of 94%.