Abstract

COVID-19 is a disease caused by a virus from the coronavirus group, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Sars-CoV-2 virus has 5 variants that are included in the variant of concern (VOC) namely Alpha, Beta, Delta, Gamma, and Omicron. The COVID-19 virus has infected more than 400 million people worldwide. This information causes a significant increase in data with the result that computations are needed to obtain knowledge (pattern) from the data. Machine learning is a tool that can facilitate the analysis of big data, one of which is classification. In this paper, we implement two boosting algorithms: eXtreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM), to classify the Deoxyribonucleic acid (DNA) sequence data from the COVID-19 virus variants. Additionally, we utilized one-hot encoded method to encode data. The experiment results showed that XGB has better accuracy than LGBM, but LGBM has faster computation time than XGB. The highest accuracy is 0.992.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
EditorsMochammad Facta, Mohammad Syafrullah, Munawar Agus Riyadi, Imam Much Ibnu Subroto, Irawan Irawan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-34
Number of pages6
ISBN (Electronic)9786239213558
DOIs
Publication statusPublished - 2022
Event9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022 - Jakarta, Indonesia
Duration: 6 Oct 20227 Oct 2022

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2022-October
ISSN (Print)2407-439X

Conference

Conference9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Country/TerritoryIndonesia
CityJakarta
Period6/10/227/10/22

Keywords

  • Boosting Algorithm
  • COVID-19
  • Classification
  • DNA Sequencing
  • One-Hot Encoded

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