Deteksi Citra X-Ray Paru-Paru Terinfeksi COVID-19 dengan Algoritma CNN berbasis Aplikasi Web

Supri Bin Hj Amir, Sitti Nur Azizah Fitriani Akbar, Hendra Hendra, Andi Muhammad Anwar, Sulfayanti Sulfayanti

Abstract


Pada penelitian ini menggunakan algoritma Convolutional Neural Network (CNN) untuk mendeteksi COVID-19 berdasarkan citra X-ray Paru-paru. Arsitektur CNN yang digunakan adalah EfficientNetB7 dan Resnet152V2 dengan memanfaatkan teknik Transfer Learning. Penelitian ini berfokus pada membandingkan kinerja kedua model arsitektur dalam mengklasifikasikan citra X-ray Paru-paru terinfeksi COVID-19. Selanjutnya mengimplementasikan model CNN tersebut ke aplikasi deteksi Citra X-ray paru-paru berbasis web. Dari hasil evaluasi kedua model tersebut disimpulkan bahwa Resnet152-V2 mencapai kinerja lebih baik dibanding arsitektur CNN EfficientNetB7 dengan akurasi 97% sedangkan EfficientNetB7 dengan akurasi 95%.


Keywords


COVID-19, CNN, EfficientNetB7, Resnet152-V2, Web Aplikasi

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References


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DOI: http://dx.doi.org/10.30872/jim.v17i1.6534

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