Saidi, Subian and Herawati, Netti and Nisa, Khoirin (2021) Nonparametric Modeling Using Kernel Method for the Estimation of the Covid-19 Data in Indonesia During 2020. International Journal of Mathematics Trends and Technology, 67. pp. 136-144. ISSN 2231 – 5373

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One of the most research discussed topics is the prediction or forecasting of the COVID-19 data using the classical time series (such as exponential smoothing) and machine learning methods. In fact, the classical time series method often produces quite large error rates. In this study, the researchers try to use nonparametric modeling with the kernel method to get better results with the smallest error rate. Furthermore, the results of the kernel method are compared with the results of the classical time series method. As a comparison tool, the researchers use MAPE by paying attention to the smallest MAPE value. The data used in this study are the COVID-19 data in Indonesia in which its variable is the total of deaths per day. After comparing the classical time series method with the kernel method, the obtained better results are the results from the kernel method. In this study, the researchers use five kernel functions, namely the Gaussian, Epanechnikov, Triangular, Biweight, and Triweight. Then, these five kernel functions are compared to find the best function. After the comparison process is done, the triweight kernel function was determined as the best function with the smallest error rate with a MAPE value of 0,9%. S Keywords — Covid-19, Kernel Method, Mean Absolute Percentage Error, Time Series, Triweight Kernel Function.

Item Type: Article
Subjects: Q Science > Q Science (General)
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika
Depositing User: SUBIAN SAIDI
Date Deposited: 20 Sep 2021 08:12
Last Modified: 20 Sep 2021 08:12

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