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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Techne : Jurnal Ilmiah Elektroteknika Journal of Biomedical Science and Bioengineering Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Elvira Sukma Wahyuni
Universitas Islam Indonesia
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Journal : Journal of Biomedical Science and Bioengineering

 1 
The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray Elvira Sukma Wahyuni
Journal of Biomedical Science and Bioengineering Vol 2, No 1 (2022)
Publisher : Center for Biomechanics, Biomaterials, Biomechantronics and Biosignal Processing (CBOIM3S)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbiomes.2022.v2i1.1-6

Abstract

COVID-19 has become a pandemic spread to nearly all countries in the world. This virus has caused many deaths. Screening using a chest X-ray is an alternative to find out positive COVID-19 patients. Chest X-ray is advantageous because every hospital must have an X-ray device so that hospitals do not need additional equipment to detect COVID-19-positive patients. This study aims to compare the machine learning models of Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression to predict COVID-19 positive patients. The stages of the research carried out by this study are the Pre-process stage, feature extraction, and classification. The results showed that the Naïve Bayes classification method got the highest performance with an accuracy of 95.24%.
Co-Authors Abdul Manan Almira Budiyanto Alvita Widya Kustiawan Putri Dzata Farahiya Farahiya, Dzata Firdaus Firmansyah Nur Budiman, Firmansyah Nur Hendra Setiawan Idha Arfianti Wiraagni Iqbal, Zulfika Mohammad Brado Frasetyo Muhammad Hendri Nisa Agustin Pratiwi Pelu Yogi Agus Priatna Zulfika Iqbal