Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction

Mohammad Reza Faisal

reza.faisal@ulm.ac.id
Lambung Mangkurat University (Indonesia)

Muhammad Thoriq Hidayat


Lambung Mangkurat University (Indonesia)

Dwi Kartini


Lambung Mangkurat University (Indonesia)

Fatma Indriani


Lambung Mangkurat University (Indonesia)

Irwan Budiman


Lambung Mangkurat University (Indonesia)

Triando Hamonangan Saragih


Lambung Mangkurat University (Indonesia)

Abstract

Early detection for COVID-19 has now been widely developed. One of the methods used is cough audio detection. This research aims to classify cough audio. Audio feature extraction is performed using MFCC to obtain numerical features. Feature classification is done using SVM, Random Forest, and Naive Bayes methods. Evaluation is done to find the best classification method. The evaluation results in this study show that SVM Kernel RBF produces the best evaluation value with an AUC value of 0.657715.


Keywords:

audio cough, SVM, Random Forest, Naive Bayes

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Published
2023-12-29

Cited by

Faisal, M. R., Hidayat, M. T., Kartini, D., Indriani, F., Budiman, I., & Saragih, T. H. (2023). Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction. Journal of Computer Sciences Institute, 29, 399–404. https://doi.org/10.35784/jcsi.4447

Authors

Mohammad Reza Faisal 
reza.faisal@ulm.ac.id
Lambung Mangkurat University Indonesia

Authors

Muhammad Thoriq Hidayat 

Lambung Mangkurat University Indonesia

Authors

Dwi Kartini 

Lambung Mangkurat University Indonesia

Authors

Fatma Indriani 

Lambung Mangkurat University Indonesia

Authors

Irwan Budiman 

Lambung Mangkurat University Indonesia

Authors

Triando Hamonangan Saragih 

Lambung Mangkurat University Indonesia

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