Tiara Zahra, . (2025) KLASIFIKASI PENYAKIT PADA DAUN SELADA MENGGUNAKAN ARSITEKTUR RESNET-50 DENGAN TRANSFER LEARNING DAN FINE-TUNING. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This paper discusses the development and evaluation of a deep learning model based on ResNet-50 to classify lettuce leaf diseases, utilizing transfer learning and fine-tuning. The study involved collecting 800 images across four classes, with preprocessing steps such as resizing, normalization, and augmentation. The model was trained and tested to improve accuracy and robustness, achieving a validation accuracy of about 95.31% and an accuracy of about 84% on the test data. The results show that combining transfer learning with fine-tuning significantly improves the model performance, especially in distinguishing visually similar conditions such as “frog eye” and bacterial leaf spot. The study shows that this approach is effective for automated disease diagnosis in agriculture, with potential for further improvement by expanding the dataset, exploring other CNN architectures, and analyzing misclassified samples to refine the model.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511018] [Pembimbing 1: Iin Ernawati] [Pembimbing 2: Radinal Setyadinsa] [Penguji 1: Ridwan Raafi’udin] [Penguji 2: Nurul Afifah Arifuddin] |
| Uncontrolled Keywords: | Disease Classification, Lettuce Leaves, Resnet-50, Transfer Learning, Fine-Tuning, Deep Learning |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QK Botany T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | TIARA ZAHRA |
| Date Deposited: | 11 Aug 2025 12:31 |
| Last Modified: | 11 Aug 2025 12:31 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37439 |
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