Elvira, Dwi (2021) Klasifikasi Citra Daun Kelapa Sawit Yang Terkena Dampak Hama Menggunakan Metode K-Nearest Neighbor (KNN). Other thesis, Universitas Islam Riau.
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Abstract
Oil palm is an important oil-producing plantation crop food, industrial oil, and biofuels. Especially for plantations people's palm oil, common problems faced include the low productivity and quality of production. One of the reasons for the low productivity of oil palm plantations is due to pest attack. One attack Leaf-eating caterpillar pests are fire caterpillars and bagworms. Potential loss of yield production caused by these two pests can reach 35%, so An application for image classification of affected oil palm leaves was developed impact of pests using the K-Nearest Neighbor (K-NN) method where to texture analysis/feature extraction using the Gray Level Co-Occurrence method Matrix (GLCM) is then classified using the K-Nearest algorithm Neighbor (KNN). Based on the results of the evaluation of the affected leaves fire caterpillars, pocket caterpillars and leaves were normal with an accuracy value of 83.3%.
Item Type: | Thesis (Other) |
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Contributors: | Contribution Contributors NIDN/NIDK Sponsor Yulianti, Ana perpustakaan@uir.ac.id |
Uncontrolled Keywords: | Daun Sawit, Aplikasi, Hama Ulat Api, Hama Ulat Kantong, GLCM, K-NN, Gray Level Co-Occurrence , K-Nearest Neighbor. |
Subjects: | Q Science > QA Mathematics > QA76 Computer software |
Divisions: | > Teknik Informatika |
Depositing User: | Febby Amelia |
Date Deposited: | 17 Mar 2022 10:21 |
Last Modified: | 17 Mar 2022 10:21 |
URI: | https://repository.uir.ac.id/id/eprint/8897 |
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