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Yeni Rismawati
Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember
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KLASIFIKASI DATA DIAGNOSIS COVID-19 MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN GENERALIZED LINEAR MODEL (GLM) Yeni Rismawati; I Made Tirta; Yuliani Setia Dewi
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

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Abstract

Covid-19 is still a global concern. From the first time, this virus was detected, on December 31, 2019. As of March 20, 2022, there were 460 million positive cases of Covid-19, with 6.06 million deaths worldwide. The high number of Covid-19 cases is due to the rapid spread of this virus. One way to prevent the spread of this virus is by early detection of the disease and mapping the influence factors .The classification method with the support vector machine (SVM) method in machine learning can predict individuals diagnosed as positive for Covid-19 and who do not use the factors considered influential. Traditionally this can also be done with a generalized linear model (GLM). This study aims to compare two methods (SVM and GLM) in predicting individuals diagnosed as positive for Covid-19. In addition, this study also conducted an ensemble between SVM and GLM to determine whether the ensemble performed could produce better accuracy than the single classifier (SVM and GLM). The results showed that the accuracy with SVM and GLM was relatively high. However, SVM is slightly superior with 98.91% accuracy, and GLM with 95.64% accuracy. Meanwhile, the ensemble of both models achieved 98.91% accuracy, as high as SVM. Keywords: Covid-19, Klasifikasi, Machine Learning SVM, GLM
Co-Authors I Made Tirta Yuliani Setia Dewi