Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification
Chili is an important agricultural commodity in Indonesia and plays an significant role in the economic growth of the country. Its demand from households and industries reaches up to 61%. However, this high demand also means that monitoring efforts must be intensified, particularly for chili plant d...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/5028 |
| _version_ | 1827359459212525568 |
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| author | Achmad Naila Muna Ramadhani Galuh Wilujeng Saraswati Rama Tri Agung Heru Agus Santoso |
| author_facet | Achmad Naila Muna Ramadhani Galuh Wilujeng Saraswati Rama Tri Agung Heru Agus Santoso |
| author_sort | Achmad Naila Muna Ramadhani |
| collection | DOAJ |
| description | Chili is an important agricultural commodity in Indonesia and plays an significant role in the economic growth of the country. Its demand from households and industries reaches up to 61%. However, this high demand also means that monitoring efforts must be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not addressed promptly, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and effective. Common chili plant diseases include chili leaf yellowing disease, chili leaf curling disease, cercospora leaf spots, and magnesium deficiency with symptoms that can be observed through the shape and color of the leaves. This research aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model performance using the Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001, produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential. |
| first_indexed | 2024-03-08T06:29:20Z |
| format | Article |
| id | doaj.art-79c24d3dbe45406a9e52ea5066cdd00c |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T06:29:20Z |
| publishDate | 2023-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-79c24d3dbe45406a9e52ea5066cdd00c2024-02-03T12:23:47ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-08-017494094610.29207/resti.v7i4.50285028Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases ClassificationAchmad Naila Muna Ramadhani0Galuh Wilujeng Saraswati1Rama Tri Agung2Heru Agus Santoso3Universitas Dian NuswantoroUniversitas Dian NuswantoroUPN Veteran YogyakartaUniversitas Dian NuswantoroChili is an important agricultural commodity in Indonesia and plays an significant role in the economic growth of the country. Its demand from households and industries reaches up to 61%. However, this high demand also means that monitoring efforts must be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not addressed promptly, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and effective. Common chili plant diseases include chili leaf yellowing disease, chili leaf curling disease, cercospora leaf spots, and magnesium deficiency with symptoms that can be observed through the shape and color of the leaves. This research aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model performance using the Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001, produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential.http://jurnal.iaii.or.id/index.php/RESTI/article/view/5028chilicomparisoncnnmobilenetv2 |
| spellingShingle | Achmad Naila Muna Ramadhani Galuh Wilujeng Saraswati Rama Tri Agung Heru Agus Santoso Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) chili comparison cnn mobilenetv2 |
| title | Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification |
| title_full | Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification |
| title_fullStr | Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification |
| title_full_unstemmed | Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification |
| title_short | Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification |
| title_sort | performance comparison of convolutional neural network and mobilenetv2 for chili diseases classification |
| topic | chili comparison cnn mobilenetv2 |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/5028 |
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