TY - GEN
T1 - Jatropha curcas disease identification using Fuzzy Neural Network
AU - Saragih, Triando Hamonangan
AU - Fajri, Diny Melsye Nurul
AU - Hamdianah, Andi
AU - Mahmudy, Wayan Firdaus
AU - Anggodo, Yusuf Priyo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Jatropha Curcas is a plant that has many functions and uses, but many part from that this plant can be attacked by disease. Expert systems can be applied in identification to help both farmers and workers to identify the disease. In this paper, the method used in the identification is Fuzzy Neural Network (FNN) that combine artificial neural networks with fuzzy logic techniques. A set of computational experiment reveal that the FNN obtains the best accuracy of 30%.
AB - Jatropha Curcas is a plant that has many functions and uses, but many part from that this plant can be attacked by disease. Expert systems can be applied in identification to help both farmers and workers to identify the disease. In this paper, the method used in the identification is Fuzzy Neural Network (FNN) that combine artificial neural networks with fuzzy logic techniques. A set of computational experiment reveal that the FNN obtains the best accuracy of 30%.
KW - Disease Identification
KW - Fuzzy Neural Network
KW - Jatropha Curcas
UR - https://www.scopus.com/pages/publications/85049380823
U2 - 10.1109/SIET.2017.8304153
DO - 10.1109/SIET.2017.8304153
M3 - Conference contribution
AN - SCOPUS:85049380823
T3 - Proceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
SP - 305
EP - 309
BT - Proceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017
Y2 - 24 November 2017 through 25 November 2017
ER -