Diny Melsye Nurul Fajri, Triando Hamonangan Saragih, Andi Hamdianah, Wayan Firdaus Mahmudy, Yusuf Priyo Anggodo
Jatropha curcas is an important commodity for farmers. The farmers must be aware of the disease caused by pest or virus for the existence and benefits of this plant. The main obstacle is the lack of farmers' knowledge about diseases and a system that utilize plant expert knowledge is needed. This paper proposes Fuzzy Neural Network (FNN) method to identify Jatropha Curcas Disease. To achieve higher accuracy, simulated annealing (SA) is employed to adjust the boundary of membership functions of the FNN. Computational experiments prove that the proposed method produces promising result and the SA is effective to improve the accuracy of the FNS. © 2017 IEEE.
Faculty of Computer Science, Brawijaya University, Malang, Indonesia
