Skripsi

KLASIFIKASI KATARAK PADA CITRA MATA MENGGUNAKAN ARSITEKTUR VGGNET

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002597	T172628	T1726282025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1726282025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, 176 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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