Skripsi

METODE ENSEMBLE PADA ARSITEKTUR VGG-16 DAN VISION TRANSFORMER DALAM KLASIFIKASI PENYAKIT DIABETIC RETINOPATHY

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Penilaian

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Diabetic retinopathy (DR) is a complication of the retina caused by diabetic disease. DR can be identified through images of the retina of the eye. One of the architectures that is often used to identify DR is VGG-16. VGG-16 is capable of handling large amounts of data and large attributes. However, the overfitting often occurs due to deep layers and multiple convolutions. Another method in deep learning that does not use convolution operations is the Vision Transformer (ViT). ViT has the advantage of not using convolution operations in the training process but using self attention. ViT tends to ignore minority classes so that the performance of the model in classifying the minority class is less optimal. The weaknesses of each architecture can be overcome by using ensemble learning. The study proposed an ensemble method that combines the results of VGG-16 and Vision Transformer using weighted voting techniques with ResNet. ResNet was used to help the model learn the weight patterns on each data and avoid over training in the classification of DR disease. The accuracy, precision, recall, and F1-score results in this study were excellent, above 95%. The ensemble method training graphs in the study showed that the study was able to cope with overfitting. The results show the ensemble method in this study is excellent and strong for the classification of diabetic retinopathy disease based on the severity of retinal image behavior, implement in 5 classes: Non Proliferative Diabetic Retinopathy (NDR), Mild, Moderate, Severe dan Proliferative Diabetic Retinopathy (PDR)

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407001378	T139613	T1396132023	Central Library (References)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1396132023

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam., 2024

Collation

xii, 82 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Penyakit Diabetes
Prodi Ilmu Matematika

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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