Vini Yulisviani, . (2025) KLASIFIKASI PENYAKIT DAUN MELON MENGGUNAKAN PERBANDINGAN ARSITEKTUR XCEPTION DAN VGG16 DENGAN METODE TRANSFER LEARNING. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Melon (Cucumis melo L) is a high-value horticultural commodity with increasing demand in both domestic and export markets. However, melon production in Indonesia has declined in recent years, partly due to disease attacks that reduce plant productivity. Manual disease detection requires specific expertise and is prone to errors, making the need for a more efficient using the Convolutional Neural Network (CNN) algorithm by comparing the Xception and VGG16 architectures, utilizing transfer learning from pre-trained ImageNet models. The dataset used consists of four classes, including three types of diseased leaves and one class of healthy leaves, totaling 1000 image divided into 80:10:10 for training, validation and testing. The results show that Xception architecture achieved the best performance with 94% accuracy, 97,50% training accuracy, and 92,67% validation accuracy, while VGG16 obtained 89% accuracy, 88,38% training accuracy, and 85% validation accuracy. The system developed is expected to assist in the fast and accurate identification on melon plant diseases.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No Panggil : 2110511028] [Pembimbing 1 : Iin Ernawati] [Pembimbing 2 : Nurul Afifah Arifuddin] [Penguji 1: Indra Permana Solihin] [Penguji 2: Muhammad Adrezo] |
| Uncontrolled Keywords: | melon, CNN, transfer learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | VINI YULISVIANI |
| Date Deposited: | 12 Aug 2025 04:13 |
| Last Modified: | 12 Aug 2025 04:13 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37464 |
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