Orun, Helda Nace (2024) Klasifikasi Penyakit Daun Tomat Menggunakan VGG Berbasis Website. (Classification Of Tomato Leaf Diseases Using Web-Based VGG). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.
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Abstract
A tomato leaf disease classification system uses Convolutional Neural Network (CNN) architecture, specifically VGG to help farmers accurately diagnose diseases on tomato plants. Leaf blight and bacterial leaf spot are the most common tomato leaf diseases in Indonesia, inhibiting plant growth and resulting in poor quality. Detection of diseases on tomato leaves is important to prevent agricultural production losses. Recent technological advances in artificial intelligence and machine learning can help farmers accurately identify diseases on tomato plants using training and validation datasets. Some previous studies using the CNN method were able to achieve a fairly high level of accuracy. From the results of this study, researchers have achieved a Train Accuracy of 85% and Validation Accuracy of 82% from train data of 25,848 images and valid data of 6,688 then have implemented the model to a classification system on a website platform that can help farmers accurately identify tomato leaf diseases.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Convolutional Neural Network; Tomato Leaf; Implementation; Classification; VGG |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Teknik > Program Studi Teknik Informatika |
| Depositing User: | 1462000030 Helda Nace Orun |
| Date Deposited: | 03 Oct 2001 19:22 |
| Last Modified: | 03 Oct 2001 19:22 |
| URI: | http://repository.untag-sby.ac.id/id/eprint/41355 |
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