Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network
| Main Authors: | Wicaksono, Guntur, Andryana, Septi, -, Benrahman |
|---|---|
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
Universitas Widyagama Malang
, 2020
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| Subjects: | |
| Online Access: |
http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs/article/view/1221 http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs/article/view/1221/1019 |
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article-1221 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network</title><creator>Wicaksono, Guntur</creator><creator>Andryana, Septi</creator><creator>-, Benrahman</creator><subject lang="en-US">apple leaf disease; convolutional neural networks; image classification; LeNet-5</subject><description lang="en-US">According to 2017 statistical fruit and vegetable crops published by BPS, total apple production in 2017 amounted to 319004 tons. There are many diseases that can attack apple plants, therefore early detection and identification of plant diseases are the main factors to prevent and reduce the spread of apple plant diseases. CNN method is used in this study with LeNet-5 architecture which can process 3151 imagery data with a mini-mum accuracy level of 75%. This study uses a dataset derived from PlantVillage created by SP Mohanty CEO & Co-founder of CrowdAI with a total of 3151 leaf images that have been classified according to their respec-tive classes. CNN stages include Convolution Layer, Rectified Linear Unit (ReLU), Subsampling, Flattening, Fully Connected Layer. The test results are evaluated using image testing data. The evaluation process is done using a confusion matrix. Based on the results of testing applications that are designed with 99,4% model ac-curacy and 97,8% validation accuracy, the application is useful for detecting apple disease using apple leaf images.</description><publisher lang="en-US">Universitas Widyagama Malang</publisher><contributor lang="en-US"/><date>2020-01-25</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs/article/view/1221</identifier><identifier>10.31328/jointecs.v5i1.1221</identifier><source lang="en-US">JOINTECS (Journal of Information Technology and Computer Science); Vol 5, No 1 (2020); 9-16</source><source>2541-6448</source><source>2541-3619</source><source>10.31328/jointecs.v5i1</source><language>eng</language><relation>http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs/article/view/1221/1019</relation><rights lang="en-US">Copyright (c) 2020 JOINTECS (Journal of Information Technology and Computer Science)</rights><rights lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-1221</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:Journal |
| author |
Wicaksono, Guntur Andryana, Septi -, Benrahman |
| title |
Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network |
| publisher |
Universitas Widyagama Malang |
| publishDate |
2020 |
| topic |
apple leaf disease convolutional neural networks image classification LeNet-5 |
| url |
http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs/article/view/1221 http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs/article/view/1221/1019 |
| contents |
According to 2017 statistical fruit and vegetable crops published by BPS, total apple production in 2017 amounted to 319004 tons. There are many diseases that can attack apple plants, therefore early detection and identification of plant diseases are the main factors to prevent and reduce the spread of apple plant diseases. CNN method is used in this study with LeNet-5 architecture which can process 3151 imagery data with a mini-mum accuracy level of 75%. This study uses a dataset derived from PlantVillage created by SP Mohanty CEO & Co-founder of CrowdAI with a total of 3151 leaf images that have been classified according to their respec-tive classes. CNN stages include Convolution Layer, Rectified Linear Unit (ReLU), Subsampling, Flattening, Fully Connected Layer. The test results are evaluated using image testing data. The evaluation process is done using a confusion matrix. Based on the results of testing applications that are designed with 99,4% model ac-curacy and 97,8% validation accuracy, the application is useful for detecting apple disease using apple leaf images. |
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