Skripsi

KLASIFIKASI PNEUMONIA MELALUI CITRA CHEST X-RAY MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ARSITEKTUR EFFICIENTNETV2

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Pneumonia is a lung infection with a high mortality rate, ranking as the fourth leading cause of global deaths in 2020 according to the WHO. In this study, Chest X-Ray images were classified into pneumonia and normal categories using the Convolutional Neural Network (CNN) method with EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, and EfficientNetV2-B3 architectures, which were subsequently implemented into a website-based application. The PneumoniaMNIST dataset consisting of 5,856 images was used, divided into 4,708 training data, 524 validation data, and 624 testing data. The preprocessing stages conducted included RGB conversion, resizing, data augmentation, and handling class imbalance using class weights. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The research results showed that the best EfficientNetV2 model was achieved using the EfficientNetV2-B2 architecture with a batch size of 32 and a learning rate of 0.0001, resulting in an accuracy of 95.35%, precision of 97.13%, recall of 95.38%, and F1-score of 96.25%.

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Detail Information

Series Title

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Call Number

T1989592026

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xiii, 130 hlm.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Klasifikasi Citra
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

KA

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