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Klasifikasi penyakit citra daun anggur menggunakan model CNN transfer learning VGG16

Grape leaf image disease classification using CNN transfer learning model VGG16

Department of Computer Science, Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri. Jalan Kramat Raya No. 18, Senen, Jakarta Pusat, Indonesia

Received: 17 Dec 2020; Revised: 26 Jun 2021; Accepted: 5 Jul 2021; Available online: 7 Aug 2021; Published: 31 Oct 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and classification uses CNN. The results of this study obtained the accuracy of the CNN model training of 99.50%. Testing using test data yields an accuracy of 97.25 % while using test image data outside the dataset obtained an accuracy of 95%. The designed image processing method is expected to be applied in designing a system to identify and classify disease images on grape leaves.
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Keywords: k-means; transfer learning VGG16; klasifikasi; CNN
Funding: Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

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