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All Journal International Journal of Electronics and Communications Systems
Dzihan Septiangraini
Universitas Lampung
Computer Science & IT Electrical & Electronics Engineering

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Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification Yessi Mulyani; Dzihan Septiangraini; Meizano Ardhi Muhammad; Gigih Forda Nama
International Journal of Electronics and Communications Systems Vol 2, No 2 (2022): International Journal of Electronics and Communications System
Publisher : Raden Intan State Islamic University of Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1163.598 KB) | DOI: 10.24042/ijecs.v2i2.13694

Abstract

Convolutional Neural Network (CNN) is very good at classifying images. To measure the best CNN architecture, a study must be done against real-case scenarios. Aglaonema, one of the plants with high similarity, is chosen as a test case to compare CNN architecture. In this study, a classification process was carried out on five classes of Aglaonema imagery by comparing five architectures from the Convolutional Neural Network (CNN) method: LeNet, AlexNet, VGG16, Inception V3, and ResNet50. The total dataset used is 500 image data, with the distribution of training data by 80% and test data by 20%. The segmentation process is performed using the Grabcut algorithm by separating the foreground and background. To build a model for CNN architecture using Google Colab and Google Drive storage. The results of the tests carried out on five classes of Aglaonema images obtained the best accuracy, precision, and recall results on the Inception V3 architecture with values of 92.8%, 93%, and 92.8%. The CNN architecture has the highest level of accuracy in classifying aglaonema plant types based on images. This study seeks to close research gaps, contribute to the field of research, and serve as a platform for primary prevention research.
Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification Yessi Mulyani; Dzihan Septiangraini; Meizano Ardhi Muhammad; Gigih Forda Nama
International Journal of Electronics and Communications Systems Vol 2, No 2 (2022): International Journal of Electronics and Communications System
Publisher : Raden Intan State Islamic University of Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v2i2.13694

Abstract

Convolutional Neural Network (CNN) is very good at classifying images. To measure the best CNN architecture, a study must be done against real-case scenarios. Aglaonema, one of the plants with high similarity, is chosen as a test case to compare CNN architecture. In this study, a classification process was carried out on five classes of Aglaonema imagery by comparing five architectures from the Convolutional Neural Network (CNN) method: LeNet, AlexNet, VGG16, Inception V3, and ResNet50. The total dataset used is 500 image data, with the distribution of training data by 80% and test data by 20%. The segmentation process is performed using the Grabcut algorithm by separating the foreground and background. To build a model for CNN architecture using Google Colab and Google Drive storage. The results of the tests carried out on five classes of Aglaonema images obtained the best accuracy, precision, and recall results on the Inception V3 architecture with values of 92.8%, 93%, and 92.8%. The CNN architecture has the highest level of accuracy in classifying aglaonema plant types based on images. This study seeks to close research gaps, contribute to the field of research, and serve as a platform for primary prevention research.
Co-Authors Gigih Forda Nama Meizano Ardhi Muhammad Mulyani, Yessi