KLASIFIKASI KATARAK PADA CITRA MATA MENGGUNAKAN ARSITEKTUR VGGNET

SU'UDIYAH, NUR and Darmawahyuni, Annisa (2025) KLASIFIKASI KATARAK PADA CITRA MATA MENGGUNAKAN ARSITEKTUR VGGNET. Undergraduate thesis, Sriwijaya University.

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Abstract

Cataract is the leading cause of blindness, including in Indonesia, with 1.6 million reported cases. Early detection is crucial, yet access to eye healthcare remains limited, especially in remote areas, and is further hindered by economic constraints and a shortage of ophthalmologists. This study develops a cataract classification model based on fundus images using VGG-16 and VGG-19 architectures through a transfer learning approach. The dataset is categorized into three classes: normal, cataract, and glaucoma, and split with a 90:10 ratio. The best-performing model, VGG-19 with 50 epochs, a learning rate of 0.01, and batch size of 16, achieved 76% accuracy. The results demonstrate a reasonably good performance in detecting cataracts automatically and efficiently.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Katarak, Citra Fundus Retina, VGGNet, Transfer Learning, Kecerdasan Buatan
Subjects:	T Technology > T Technology (General) > T58.6-58.62 Management information systems > T58.6 General works Industrial engineering Information technology. Information systems (General) Management information systems -- Continued
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nur Su'udiyah
Date Deposited:	15 May 2025 08:20
Last Modified:	15 May 2025 08:20
URI:	http://repository.unsri.ac.id/id/eprint/172628

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