Mulyani, Yessi and Dzihan, Septiangraini and Muhammad, Meizano Ardhi and Nama, Gigih Forda (2022) Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification. International Journal of Electronics and Communications System, 2 (2). ISSN ISSN: 2798-2610

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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.

Item Type:	Article
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Fakultas Teknik (FT) > Prodi Teknik Informatika
Depositing User:	Meizano Ardhi Muhammad
Date Deposited:	31 Mar 2023 00:49
Last Modified:	31 Mar 2023 00:49
URI:	http://repository.lppm.unila.ac.id/id/eprint/49470

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• Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification. (deposited 29 Mar 2023 08:12)
  • Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification. (deposited 31 Mar 2023 00:49) [Currently Displayed]

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