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

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DOI

Mohammad Reza Faisal

reza.faisal@ulm.ac.id

Muhammad Thoriq Hidayat

1911016210010@mhs.ulm.ac.id

Dwi Kartini

dwikartini@ulm.ac.id

Fatma Indriani

f.Indriani@ulm.ac.id

Irwan Budiman

irwan.budiman@ulm.ac.id

Triando Hamonangan Saragih

triando.saragih@ulm.ac.id

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

References

Article Details

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