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Akbar Ganang Pradana
Universitas Dian Nuswantoro
Computer Science & IT

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Journal : Journal of Information System and Application Development

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Fine tuning model Convolutional Neural Network EfficientNet-B4 dengan augmentasi data untuk klasifikasi penyakit kakao Akbar Ganang Pradana; De Rosal Ignatius Moses Setiadi; Ahmad Rofiqul Muslikh
Journal of Information System and Application Development Vol 2, No 1 (2024): Maret 2024
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jisad.v2i1.11899

Abstract

Cocoa is an important agricultural commodity in Indonesia which contributes to the economy with a production share of 15.68%. Cocoa diseases, such as Black Pod Rot and Pod Borer, are very detrimental to farmers. So it is necessary to build a recognition model that can classify automatically with high performance. Unfortunately the collected dataset is very unbalanced, and this is an additional challenge as it can reduce recognition performance. This study proposes disease recognition in cocoa images using the EfficientNet-B4 Convolutional Neural Network (CNN) model with fine-tuning. In this study also used seven kinds of data augmentation. The result is that the proposed CNN model has a high accuracy of 97.3% which is an increase of about 7.4% compared to the original model, at relatively few epochs. In addition, the proposed model is compared with other CNN models such as Xception, InceptionV3, ResNet, DenseNet, and EfficientNet, using the same approach, namely fine-tuning and epoch. The result is that the proposed method is superior to other models. This confirms that the proposed CNN model can also work better on unbalanced data.
Co-Authors Ahmad Rofiqul Muslikh De Rosal Ignatius Moses Setiadi