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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Optimization of Dempster-Shafer’s Believe Value Using Genetic Algorithm for Identification of Plant Diseases Jatropha Curcas Triando Hamonangan Saragih; Wayan Firdaus Mahmudy; Yusuf Priyo Anggodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp61-68

Abstract

Jatropha curcas is a plant that can be used as a substitute for diesel fuel. Lack of knowledge of farmers and the limited number of experts and extension agents into the problem of dealing with the disease Jatropha curcas plant which resulted in lower quality of Jatropha curcas. Dempster-Shafer method can be a solution for decision making based on previous research. The difference in beliefs of every expert in seeing Jatropha diseases are important because Dempster-Shafer can not solve this problem. Optimization using genetic algorithms can solve this problem. Optimization of belief values using genetic algorithms can improve the accuracy of the results of this system are using Dempster-Shafer. On the results of this system provides the highest system accuracy value, opimization of belief values using genetic algorithms gives a more significant result than the use of Dempster-Shafer only.
Jatropha Curcas Disease Identification With Extreme Learning Machine Triando Hamonangan Saragih; Diny Melsye Nurul Fajri; Wayan Firdaus Mahmudy; Abdul Latief Abadi; Yusuf Priyo Anggodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp883-888

Abstract

Jatropha is a plant that has many functions, but this plant can be attacked by various diseases. Expert systems can be applied in identifying so that can help both farmers and extension workers to identify the disease. one of method that can be used is Extreme Learning Machine. Extreme Learning Machine is a method of learning in Neural Network which has a one-time iteration concept in each process. In this study get a maximum accuracy of 66.67% with an average accuracy of 60.61%. This proves the identification using Extreme Learning Machine is better than the comparison method that has been done before.