Implementasi Metode Naïve Bayes Untuk Identifikasi Penyakit Tanaman Kubis Berdasarkan Citra Daun
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Institusion
Politeknik Negeri Jember
Author
Elisa, Qothrun Nada
Subject
462 - Teknologi Informasi
Datestamp
2022-05-31 02:14:48
Abstract :
Cabbage is one of the horticultural crops with high economic value, according to the Ministry of Agriculture, cabbage is also an export commodity that contributes to the National PDB. Cabbage production in Indonesia experiences instability or fluctuating production every year. This is caused by several factors, including disease problems, one of which is black rot and soft rot, the symptoms of which are on the leaves. Disease detection in cabbage is still done manually, therefore there is a need for a system to identify diseases in cabbage leaves. The method used in this study is Nave Bayes using an image with a green component obtained from an RGB image, while texture-based feature extraction uses a Gray Level Co-Occurance Matrix (GLCM), with the parameters used are contrast, correlation, homogeneity, and energy with an angle of 135°. The percentage of research using the Naïve Bayes method is able to classify and identify cabbage leaf diseases with an accuracy of 85%.
Cabbage is one of the horticultural crops with high economic value, according to the Ministry of Agriculture, cabbage is also an export commodity that contributes to the National PDB. Cabbage production in Indonesia experiences instability or fluctuating production every year. This is caused by several factors, including disease problems, one of which is black rot and soft rot, the symptoms of which are on the leaves. Disease detection in cabbage is still done manually, therefore there is a need for a system to identify diseases in cabbage leaves. The method used in this study is Nave Bayes using an image with a green component obtained from an RGB image, while texture-based feature extraction uses a Gray Level Co-Occurance Matrix (GLCM), with the parameters used are contrast, correlation, homogeneity, and energy with an angle of 135°. The percentage of research using the Naïve Bayes method is able to classify and identify cabbage leaf diseases with an accuracy of 85%.
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