Fuzzy Expert System for Classifying Pests and Diseases of Paddy Using Bee Colony Algorithms
Yovita Tunardi, Suharjito Suharjito · International Review on Computers and Software (IRECOS) · 2016
Agriculture sector has an important role in Indonesia. However,there are still many problems to deal with, such as pests and diseases, especially for paddy which is an important crop because it is the main food for people in Indonesia. Many things have been done by the government to solve that problem, but it has not been resolved because of the lack of knowledge of Indonesian farmers. This research proposes a new way to solve that problem by applying optimized Fuzzy Logic (Fuzzy K-Means and Fuzzy C-Means) with Bee Colony to get pest and disease classifications for paddy based on existing symptoms. Bee Colony will be used to determine the initial centroid for Fuzzy Logic. The proposed approach is evaluated based on the accuracy by comparing the value of Mean Square Error (MSE) and the accuracy of every method for pest and disease classifications. MSE of the new model is smaller than regular Fuzzy Logic by 0.016 for FCM and 1.213 for FKM. The accuracy of the new model is greater than regular Fuzzy Logic by 13.87% for FCM and 18.39% for FKM. It can be concluded that Bee Colony can be used to optimize Fuzzy Logic for pest and disease classifications for paddy with better classification results.