Image of IMPLEMENTASI DEEP LEARNING UNTUK KLASIFIKASI PENYAKIT MATA DENGAN STUDI PERBANDINGAN ARSITEKTUR INCEPTIONV3 DAN VGG-16 PADA CONVOLUTIONAL NEURAL NETWORK (CNN)

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IMPLEMENTASI DEEP LEARNING UNTUK KLASIFIKASI PENYAKIT MATA DENGAN STUDI PERBANDINGAN ARSITEKTUR INCEPTIONV3 DAN VGG-16 PADA CONVOLUTIONAL NEURAL NETWORK (CNN)

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Retinal image based eye disease classification is an important approach to support automated diagnosis in the medical field. This study aims to classify four types of eye diseases normal, cataract, glaucoma, and diabetic retinopathy using Convolutional Neural Network (CNN) models with the InceptionV3 and VGG-16 architectures. The dataset was obtained from the Kaggle platform, consisting of 4,217 images, and was augmented to 11,531 images using techniques such as flipping, zooming, and scaling. The preprocessing stage included normalization adapted to each architecture and data splitting into training, validation, and testing subsets. The models were evaluated based on varying hyperparameters, including the number of epochs, batch sizes, and learning rates. The results show that the bestperforming model using the InceptionV3 architecture (100 epochs, batch size 64, learning rate 0.001) achieved a testing accuracy of 98.6%, with precision, recall, and F1-score all reaching 0.99. Meanwhile, the best VGG-16 model achieved a maximum accuracy of 89.8% with an F1-score of 0.90. In conclusion, the InceptionV3 architecture outperforms VGG-16 in classifying retinal images, and the selection of appropriate hyperparameters significantly influences the final model performance.

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Inventory Code Barcode Call Number Location Status
2507005599T183471T1834712025Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1834712025
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvi, 58 hlm.; ilus,; tab, 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.407
Content Type
Text
Media Type
unmediated
Carrier Type
other (computer)
Edition
-
Subject(s)
Prodi Sistem Komputer
Pola Pengenalan Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related
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