Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)

<Previous Article In Volume
Next Article In Volume>

Early Detection of COVID-19 Infection Without Symptoms (Asymptomatic) with a Support Vector Machine (SVM) Model Through Voice Recording of Forced Cough

Authors
Ni Nyoman Wahyuni Indraswari1, *, I Gede Pasek Suta Wijaya1, Arik Aranta1, Rani Farinda2
1University of Mataram, Mataram, 83115, Indonesia
2Vistula University, 02-787, Warsaw, Poland
*Corresponding author. Email: nunikyuni0300@gmail.com
Corresponding Author
Ni Nyoman Wahyuni Indraswari
Available Online 26 December 2022.
DOI
10.2991/978-94-6463-084-8_25How to use a DOI?
Keywords
Accuracy; Asymptomatic; Forced cough; COVID-19; SVM Model
Abstract

COVID-19 is an infectious disease caused by a coronavirus which spreads from direct human contact through droplets of mucus in the respiratory tract of an infected person. The American Centers for Disease Control and Prevention (CDC) says that asymptomatic COVID-19 patients may account for more than 50% of the transmission rate. This research uses the SVM (Support Vector Machine) model as a feature extraction processor from voice data in the training and testing process, so that it can detect asymptomatic COVID-19 from the extraction of cough voice recordings. Of the 171 subjects studied, 120 subjects (70%) for training data and 51 (30%) for test data. The data is divided into the SMOTE data and without the SMOTE data process. The results of the two data have an average performance matrix of above 80%, with accuracy for without the SMOTE data of 98.3% and for SMOTE data of 100%.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)
Series
Advances in Computer Science Research
Publication Date
26 December 2022
ISBN
10.2991/978-94-6463-084-8_25
ISSN
2352-538X
DOI
10.2991/978-94-6463-084-8_25How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ni Nyoman Wahyuni Indraswari
AU  - I Gede Pasek Suta Wijaya
AU  - Arik Aranta
AU  - Rani Farinda
PY  - 2022
DA  - 2022/12/26
TI  - Early Detection of COVID-19 Infection Without Symptoms (Asymptomatic) with a Support Vector Machine (SVM) Model Through Voice Recording of Forced Cough
BT  - Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)
PB  - Atlantis Press
SP  - 282
EP  - 297
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-084-8_25
DO  - 10.2991/978-94-6463-084-8_25
ID  - Indraswari2022
ER  -