Development of Android-based Rabbit Disease Expert System

Many rabbit keepers or breeders are panics when their rabbit has an illness. This paper proposed an expert diagnostic system application for Android-based rabbit disease using the Naïve Bayes method to determine the illness and Certainty Factor for the trust value of the condition by combining the rate of the trust of users and experts due to diagnose the diseases of the rabbit. The testing was using 65 data learning and 160 data learning to test the naïve Bayes method. Furthermore, the certainty factor is using CF user 1 and its variation. The results obtained for 65 data learning is 53%, while 160 data learning is 73%. With the naïve Bayes method, it can be concluded that the more data learning, the better and more accurate the system. The results of conformity with the testing data obtained from the variative CF user value, namely 53% accordingly, 13% inappropriate, 33% near. The effect of compliance with the sample data collected from the CF value of user 1 is 53% appropriate, 7% inappropriate, 40% is near. With the certainty factor method, it can be concluded that differences in user input values ​​affect the overall CF value.