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METODE ENSEMBLE LEARNING TEKNIK WEIGHTED AVERAGE DENGAN PEMBELAJARAN SQUEEZENET PADA ARSITEKTUR MOBILENETV2 DAN EFFICIENTNET-B0 DALAM KLASIFIKASI PENYAKIT MATA

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Penilaian

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Eye diseases such as cataracts, diabetic retinopathy (DR), glaucoma are conditions that cause visual impairment to blindness. Automatic early detection can be done by classifying eye images, one of which uses Deep Learning methods. Effective deep learning approaches in eye image classification include Convolutional Neural Network (CNN). Various CNN architectures such as MobileNetV2 and EfficientNet-B0, have been developed to improve classification performance. MobileNetV2 is known as a lightweight architecture with high computational efficiency through Depthwise Separable Convolution, but has limitations in capturing the relationship between channels. EfficientNet-B0 uses Compound Scaling and Mobile Inverted Bottleneck Convolution (MBConv) to achieve computational efficiency, but the relatively small number of parameters makes EfficientNet-B0 less optimal in handling complex datasets. The performance of MobileNetV2 and EfficientNet-B0 can be improved by combining the classification results of both architectures using ensemble learning methods. This research applies ensemble learning weighted average technique through SqueezeNet learning. SqueezeNet was used for learning because it is a simple architecture that relies on fire modules, making the model faster to train and reducing the risk of overfitting in learning the weights. The average result of ensemble learning performance obtained 95% accuracy indicates the model is very good at predicting eye diseases correctly. Sensitivity of 95% indicates the model is sensitive to the normal class. Specificity of 98% indicates accuracy in predicting eye disease classes. F1-score 95% indicates the model is balanced in distinguishing each class. Cohen's Kappa 93% shows the consistency of the prediction with the actual class. These results provide an improvement over the single classification results with an accuracy of 14%, specificity of 16%, sensitivity of 5%, F1-score of 15%, and Cohen's Kappa of 20%. The performance evaluation results per class show very good performance, but it is still quite low in the DR class, which is still below 90%. The results prove that the proposed ensemble learning method is effective in the classification of eye diseases and is able to improve the performance results of a single classification.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003136	T174146	T1741462025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1741462025

Publisher

: Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

vii, 74 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

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Media Type

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Carrier Type

-

Edition

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Subject(s)

Matematika

Specific Detail Info

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Statement of Responsibility

EM

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