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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer)
Iwan Iwut Tritosmoro
Universitas Telkom
Computer Science & IT

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IDENTIFIKASI COVID-19 BERDASARKAN CITRA X-RAY PARU-PARU MENGGUNAKAN METODE LOCAL BINARY PATTERN DAN RANDOM FOREST Afifah Amatulla Suaib; Iwan Iwut Tritosmoro; Nur Ibrahim
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 5 No 2 (2022)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v5i2.540

Abstract

Covid-19 is a phenomenon that can’t be forgotten by the world. At the end of 2019 in Wuhan, China SarsCov-2 virus was discovered until the World Health Organization declared Covid-19 as pandemic on March 9, 2020. The rapid development and transmission of this virus was overwhelmed. One way to find out someone is positive for Covid-19 is by looking at the X-Ray results of their lungs. The X-Ray results will be analyzed to determine the state of a person's lungs. The method used in this study consists of feature extraction method using Local Binary Pattern (LBP) and classification method using Random Forest. This study uses training data and test data of X-Ray images of the lungs which are divided into three classes that is normal lungs, positive for Covid-19, and Pneumonia. Based on the results of tests that have been carried out using 1,200 images divided into 900 training data and 300 test data, the system can identify Covid-19 based on X-Ray images of the lungs and classify them into three classes. The highest accuracy results obtained 85.67% using variations of image resizing= 200x200 pixel, radius of LBP= 8, and the number of trees in Random Forest=200.
Co-Authors Afifah Amatulla Suaib Nur Ibrahim