ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS
Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges...
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Universitas Pattimura
2024-05-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12156 |
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| author | Nabila Ayunda Sovia Ni Wayan Surya Wardhani |
| author_facet | Nabila Ayunda Sovia Ni Wayan Surya Wardhani |
| author_sort | Nabila Ayunda Sovia |
| collection | DOAJ |
| description | Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensemble learning approach that combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Bagging methods. To address the imbalance issue inherent in cabbage pest data, we employ the Adaptive Synthetic Sampling (ADASYN) resampling technique. The CNN acts as the primary image identifier and classifier for various cabbage pests. Subsequently, the CNN model is integrated into SVM and Bagging models to mitigate the challenges of imbalanced data in pest classification. The research outcomes demonstrate that our ensemble approach, in conjunction with the ADASYN resampling technique, achieves an impressive accuracy rate of 97%, signifying its potential for improved cabbage pest detection and classification. |
| format | Article |
| id | doaj-art-9c62a9fd7e2b4525907007aff439d7a7 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-9c62a9fd7e2b4525907007aff439d7a72025-08-20T03:37:31ZengUniversitas PattimuraBarekeng1978-72272615-30172024-05-011821237124810.30598/barekengvol18iss2pp1237-124812156ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTSNabila Ayunda Sovia0Ni Wayan Surya Wardhani1Departement of Statistics Faculty of Sciences, Brawijaya University, IndonesiaDepartement of Statistics Faculty of Sciences, Brawijaya University, IndonesiaImage classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensemble learning approach that combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Bagging methods. To address the imbalance issue inherent in cabbage pest data, we employ the Adaptive Synthetic Sampling (ADASYN) resampling technique. The CNN acts as the primary image identifier and classifier for various cabbage pests. Subsequently, the CNN model is integrated into SVM and Bagging models to mitigate the challenges of imbalanced data in pest classification. The research outcomes demonstrate that our ensemble approach, in conjunction with the ADASYN resampling technique, achieves an impressive accuracy rate of 97%, signifying its potential for improved cabbage pest detection and classification.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12156adasynbaggingcabbageconvolutional neural networksupport vector machine |
| spellingShingle | Nabila Ayunda Sovia Ni Wayan Surya Wardhani ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS Barekeng adasyn bagging cabbage convolutional neural network support vector machine |
| title | ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS |
| title_full | ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS |
| title_fullStr | ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS |
| title_full_unstemmed | ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS |
| title_short | ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS |
| title_sort | ensemble cnn with adasyn for multiclass classification on cabbage pests |
| topic | adasyn bagging cabbage convolutional neural network support vector machine |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12156 |
| work_keys_str_mv | AT nabilaayundasovia ensemblecnnwithadasynformulticlassclassificationoncabbagepests AT niwayansuryawardhani ensemblecnnwithadasynformulticlassclassificationoncabbagepests |