Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction
Mohammad Reza Faisal
reza.faisal@ulm.ac.idLambung 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 BayesReferences
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Authors
Muhammad Thoriq HidayatLambung Mangkurat University Indonesia
Authors
Dwi KartiniLambung Mangkurat University Indonesia
Authors
Fatma IndrianiLambung Mangkurat University Indonesia
Authors
Irwan BudimanLambung Mangkurat University Indonesia
Authors
Triando Hamonangan SaragihLambung Mangkurat University Indonesia
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