PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN KEMBANG KOL MENGGUNAKAN CITRA DAUN
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
AURA, CHINDY
Agtriadi, Herman Bedi
Kuswardani, Dwina
Agtriadi, Herman Bedi
Kuswardani, Dwina
Subject
Teknik Informatika
Datestamp
2023-06-05 03:56:22
Abstract :
Agriculture is the main sector that plays an important role in the national economy. Indonesia is an agricultural country where the livelihood of the majority of the population is farming. Cauliflower has an important role for human health because it contains vitamins and minerals that the body really needs, so that the demand for this vegetable continues to grow rapidly. This study aims to identify diseases in cauliflower plants with leaf objects which result in poor cauliflower quality using artificial neural networks. The method used in this study is the Convolutional Neural Network (CNN). This CNN is the result of the development of Multilayer Perception (MLP) which is used to manage two-dimensional data. CNN is a deep learning method. The CNN method also includes the type of neural network used in image data. This study uses the CNN architecture to find the best accuracy value. The data in this study were divided into 3 types, namely Training, Validation, and Test where the total data was 483. This research has succeeded in detecting disease in leaf images automatically with the best training accuracy obtained at 97.92%
Agriculture is the main sector that plays an important role in the national economy. Indonesia is an agricultural country where the livelihood of the majority of the population is farming. Cauliflower has an important role for human health because it contains vitamins and minerals that the body really needs, so that the demand for this vegetable continues to grow rapidly. This study aims to identify diseases in cauliflower plants with leaf objects which result in poor cauliflower quality using artificial neural networks. The method used in this study is the Convolutional Neural Network (CNN). This CNN is the result of the development of Multilayer Perception (MLP) which is used to manage two-dimensional data. CNN is a deep learning method. The CNN method also includes the type of neural network used in image data. This study uses the CNN architecture to find the best accuracy value. The data in this study were divided into 3 types, namely Training, Validation, and Test where the total data was 483. This research has succeeded in detecting disease in leaf images automatically with the best training accuracy obtained at 97.92%
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