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Agus Eko
Universitas Muhammadiyah Malang
Computer Science & IT Engineering

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Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm Melly Damara Chaniago; Amellia Amanullah Sugiharto; Qhistina Dyah Khatulistiwa; Zamah Sari; Agus Eko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.067 KB) | DOI: 10.29207/resti.v6i2.3823

Abstract

Corona Virus, also known as COVID-19, is one of the new viruses in 2019. Viruses caused by an animal or human diseases are called coronaviruses. Coronavirus will direct respiration in humans. Humans who are exposed to the coronavirus will experience a respiratory infection. The research that will be made helps classify X-rays of the lungs of patients affected by the coronavirus. In this study, the classification of coronaviruses focuses on three classes, namely Covid, Normal, and Viral Pneumonia. This study uses a lung X-ray image dataset. This study has four folders, namely Scenario 1, Scenario 2, Scenario 3, and Scenario 4. This study will use the Convolutional Neural Network (CNN) method by using an architectural model including Convolutional 2D, activation layers, max-pooling layer, dropout layer, flatten, and dense layer. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. The result of the accuracy of Scenario 1 is 97.87%. In Scenario 2, the accuracy is 94.84%, Scenario 3 is 91.66%, and Scenario 4 is 91.41%.
Co-Authors Amellia Amanullah Sugiharto Melly Damara Chaniago Qhistina Dyah Khatulistiwa Zamah Sari