Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction
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...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2023-12-01
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| Series: | Journal of Computer Sciences Institute |
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| Online Access: | https://ph.pollub.pl/index.php/jcsi/article/view/4447 |
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| author | Mohammad Reza Faisal Muhammad Thoriq Hidayat Dwi Kartini Fatma Indriani Irwan Budiman Triando Hamonangan Saragih |
| author_facet | Mohammad Reza Faisal Muhammad Thoriq Hidayat Dwi Kartini Fatma Indriani Irwan Budiman Triando Hamonangan Saragih |
| author_sort | Mohammad Reza Faisal |
| collection | DOAJ |
| description |
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.
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| format | Article |
| id | doaj-art-6cef1fe0f1ab4249be64bef9e59456c3 |
| institution | Kabale University |
| issn | 2544-0764 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Journal of Computer Sciences Institute |
| spelling | doaj-art-6cef1fe0f1ab4249be64bef9e59456c32025-02-02T18:02:59ZengLublin University of TechnologyJournal of Computer Sciences Institute2544-07642023-12-012910.35784/jcsi.4447Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC ExtractionMohammad Reza Faisal0Muhammad Thoriq Hidayat1Dwi Kartini2Fatma Indriani3Irwan Budiman4Triando Hamonangan Saragih5Lambung Mangkurat UniversityLambung Mangkurat UniversityLambung Mangkurat UniversityLambung Mangkurat UniversityLambung Mangkurat UniversityLambung Mangkurat University 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. https://ph.pollub.pl/index.php/jcsi/article/view/4447audio coughSVMRandom ForestNaive Bayes |
| spellingShingle | Mohammad Reza Faisal Muhammad Thoriq Hidayat Dwi Kartini Fatma Indriani Irwan Budiman Triando Hamonangan Saragih Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction Journal of Computer Sciences Institute audio cough SVM Random Forest Naive Bayes |
| title | Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction |
| title_full | Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction |
| title_fullStr | Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction |
| title_full_unstemmed | Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction |
| title_short | Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction |
| title_sort | comparison of machine learning algorithms on classification of covid 19 cough sounds using mfcc extraction |
| topic | audio cough SVM Random Forest Naive Bayes |
| url | https://ph.pollub.pl/index.php/jcsi/article/view/4447 |
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