Skripsi

KLASIFIKASI PNEUMONIA DAN NORMAL BERDASARKAN CITRA X-RAY PARU-PARU MENGGUNAKAAN CONVOLUTIONAL NEURAL NETWORK DENGAN DENSENET121

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This study aims to develop a pneumonia classification model on chest X-ray images using a Convolutional Neural Network with the DenseNet-121 architecture for early detection of lung diseases. The dataset consists of 5,856 images divided into training, validation, and testing sets. The research stages include preprocessing, image augmentation, model training using a progressive fine-tuning strategy, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the best model achieved an accuracy of 93.27%, precision of 92.36%, recall of 96.15%, and an F1-score of 94.22%. The high recall value indicates the model’s ability to minimize missed pneumonia cases. The model was also successfully implemented in a web-based application using Streamlit, demonstrating its potential as an efficient and accurate tool for early pneumonia screening based on chest X-ray images.

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Detail Information

Series Title

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Call Number

T1955372026

Publisher

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

Collation

xxiv, V-7 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.370 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Pengolahan Citra Digital
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

KA

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