Penerapan Convolutional Neural Networkpada Citra RontgenParu-Paru untuk Deteksi SARS-CoV-2

Penerapan Convolutional Neural Networkpada Citra RontgenParu-Paru untuk Deteksi SARS-CoV-2

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

COVID-19was officially declared as a pandemic by the WHOon March 11, 2020. For COVID-19, the testing methods commonly used are the Antibody Testingand RT-PCRTesting. 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 performanceof CNN in classifying x-ray image of a person’s chest to determine whether the person is suffered from COVID-19or 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

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24

Universitas Amikom Purwokerto

24 agustus 2021

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Indonesia

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