PERBANDINGAN ALGORITMA LINEAR REGRESSION DAN POLYNOMIAL REGRESSION DALAM MEMPREDIKSI JUMLAH PASIEN COVID – 19 DI INDONESIA

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TitlePERBANDINGAN ALGORITMA LINEAR REGRESSION DAN POLYNOMIAL REGRESSION DALAM MEMPREDIKSI JUMLAH PASIEN COVID – 19 DI INDONESIA
AuthorMuhammad Caissa Di Mafaza
AbstractThe 2019 corona virus pandemic called COVID-19 has brought anxiety to the world, including the country of Indonesia. COVID-19 first entered Indonesia on March 2, 2020 and the number of spreading cases continues to soar. Based on the data available on the website of the Indonesian Ministry of Health in every province in Indonesia, machine learning algorithms can predict the development of the number of cases that can be an appeal for citizens to be more vigilant. The data used is from September 1 to October 28, 2020 to predict the number of cases over the next 10 days. Several machine learning algorithms used in this research are linear regression and polynomial regression. Linear regression and polynomial regression algorithms in predicting COVID-19 in the next 10 days with many cases 359265.72, 363240.24744898, 367214.77489796, 371189.30234694,
375163.82979592, 379138.3572449, 383112.88469388, 387087.41214286, 391061.93959184, 395036.46704082
and the MAE error rate is 4195.97608843537, MSE 21811649.20598017. Based on the resulting numbers, polynomial regression has the best accuracy. It is hoped that in the future the training data used can be added with a longer period of time so that algorithms or machines can carry out deeper learning of the data so that better prediction results are obtained.
Keywordslinear regression, polynomial regression, machine learning
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Issn2302-0709
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Created At11 Jan 2022 14.02.46