Penerapan Convolutional Neural Network pada Citra Rontgen Paru-Paru
untuk Deteksi SARS-CoV-2

Penerapan Convolutional Neural Network pada Citra Rontgen Paru-Paru
untuk Deteksi SARS-CoV-2

: convolutional neural network, covid-19, image, chest, x-ray

COVID-19 was officially declared as a pandemic by the WHO on March 11, 2020. For COVID-19, the testing methods
commonly used are the Antibody Testing and RT-PCR Testing. Both methods are considered to be the most effective in
determining whether a person has been suffered from COVID-19 or not. However, alternative testing methods need to be tried.
One of them is using the Convolutional Neural Network. This study aims to measure the performance of CNN in classifying xray image of a person’s chest to determine whether the person is suffered from COVID-19 or not. The CNN model that was
built consists of 1 convolutional 2D layer, 2 activation layers, 1 maxpooling layer, 1 dropout layer, 1 flatten layer, and 1 dense
layer. Meanwhile, the chest x-ray image dataset used is the COVID-19 Radiography Database. This dataset consists of 3
classes, i.e. COVID-19 class, NORMAL class, and VIRAL_PNEUMONIA. The experiments consisted of 4 scenarios and were
carried out using Google Colab. Based on the experiments, the CNN model can achieve an accuracy of 98.69%, a sensitivity
of 97.71%, and a specificity of 98.90%. Thus, CNN has a very good performance to classify the disease based on a person’s
chest x-ray

Bambang Pilu Hartato

Universitas Amikom Purwokerto

24-08-2021

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