Hamonangan Saragih, Triando and Nur Wijayaningrum, Vivi and Haekal, Muhammad (2021) Jatropha Curcas Disease Identification using Random Forest. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 7 (1). pp. 9-17.
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Abstract
As one of the most versatile plants, Jatropha curcas is spread in various regions around the world because of the great benefits it provides. However, Jatropha curcas is easily attacked by viruses which then cause damage to the plant, such as yellowing leaves and secreting sap, making it necessary to identify Jatropha curcas disease to deal with the problem as early as possible so that the losses incurred are not too large. An expert system was built to be able to identify Jatropha curcas disease by adopting expert knowledge. The use of the Random Forest algorithm as one of the classification algorithms was applied in this study. By using a random forest, several disease prediction classes are generated by each decision tree that has been formed. The disease class with the most votes was used as the final result. In this study, the data used were 166 data with 9 diseases and 30 symptoms. The results showed that Random Forest outperformed other algorithms such as Fuzzy Neural Network and Extreme Learning Machine with an accuracy of 98.002% using the composition of training data and test data of 70:30.
| Item Type: | Artikel Umum |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisi / Prodi: | Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro) |
| Depositing User: | M.Eng. Alfian Ma'arif |
| Date Deposited: | 30 Aug 2021 02:03 |
| Last Modified: | 30 Aug 2021 02:03 |
| URI: | http://eprints.uad.ac.id/id/eprint/27540 |
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Jatropha Curcas Disease Identification using Random Forest. (deposited 21 Aug 2021 03:54)
- Jatropha Curcas Disease Identification using Random Forest. (deposited 30 Aug 2021 02:03) [Currently Displayed]
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