Klasifikasi Rontgen Citra Paru untuk COVID-19 Menggunakan Convolutional Neural Network Berbasis Arsitektur EfficientNetV2

Hutomo Try Wibowo

doi:10.51454/decode.v4i1.349

Authors

Hutomo Try Wibowo Manajemen Sistem Informasi Universitas Gunadarma

DOI:

https://doi.org/10.51454/decode.v4i1.349

Keywords:

Convulotional Neural Network, Covid-19, EfficientNetV2, Rontgen Dada

Abstract

Pada 30 Januari 2020, WHO mengumumkan wabah COVID-19 sebagai Darurat Kesehatan Masyarakat Internasional, memicu kebutuhan akan pendekatan cepat dalam diagnosis dan penilaian penyakit paru-paru. Meskipun pengamatan citra rontgen paru-paru menjadi metode yang menjanjikan, sistem penilaian manual memakan waktu, mendorong perlunya integrasi teknologi. Dalam konteks ini, perkembangan pesat visi komputer dan pembelajaran mesin memungkinkan pengembangan perangkat lunak untuk mengklasifikasikan citra rontgen secara efisien. Sejumlah penelitian telah mencatat keberhasilan berbagai arsitektur Convolutional Neural Networks (CNN) dalam pengklasifikasian penyakit paru-paru dengan hasil akurasi yang mengesankan. Studi ini memfokuskan pada eksplorasi arsitektur CNN, khususnya EfficientNetV2, dalam mengklasifikasikan citra rontgen paru ke dalam empat kelas: COVID-19, opasitas paru, Normal, dan pneumonia viral. Pada penelitian ini, proses pelatihan model menggunakan dataset COVID-19 Radiography dengan menerapkan teknik pensampellan ulang dan augmentasi beserta skema pembagian data sebagai teknik pemrosesan data, penggunaan metriks evaluasi seperti akurasi, perolehan, presisi, dan F1 juga dilakukan untuk mendapatkan model klasifikasi yang paling optimal. Hasil dari penelitian ini menunjukkan nilai metrics tertinggi yang didapatkan pada 97,05% untuk akurasi, 97,38% untuk presisi, 96,88% untuk perolehan, dan 97,13% untuk F1.

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