BibTex Citation Data :
@article{J.Gauss37864, author = {Anindita Safira and Budi Warsito and Agus Rusgiyono}, title = {ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN ALGORITMA GRID SEARCH TIME SERIES CROSS VALIDATION UNTUK PREDIKSI JUMLAH KASUS TERKONFIRMASI COVID-19 DI INDONESIA}, journal = {Jurnal Gaussian}, volume = {11}, number = {4}, year = {2023}, keywords = {Support Vector Regression (SVR); Grid Search Algorithm; Time Series Cross Validation; Kernel; MAPE (Mean Absolute Percentage Error)}, abstract = { Coronavirus Disease 2019 or Covid-19 is a group of types of viruses that interfere with the respiratory tract associated with the seafood market that emerged in Wuhan City, Hubei Province, China at the end of 2019. The first confirmed cases of Covid-19 in Indonesia on March 2, 2020, were 2 cases and until the end of 2021, it continues to grow every day. The purpose of this study was to predict the number of confirmed cases of Covid-19 in Indonesia using the Support Vector Regression (SVR) method with linear kernel functions, radial basis functions (RBF), and polynomials. Support Vector Regression (SVR) is the application of a support vector machine (SVM) in regression cases that aims to find the dividing line in the form of the best regression function. The advantage of the SVR model is can be used on time series data, data that are not normally distributed and data that is not linear. Parameter selection for each kernel used a grid search algorithm combined with time series cross validation . The criteria used to measure the goodness of the model are MSE ( Mean Square Error ), MAPE ( Mean Absolute Percentage Error ) and R 2 (Coefficient of Determination). The results of this study indicate that the best model is Support Vector Regression (SVR) with a polynomial kernel and the parameters used include Cost = 1, degree = 1, and coefficient = 0.1. The polynomial kernel SVR model produces a MAPE value of 0.4946215%, which means the model has very good predictive ability. The prediction accuracy obtained with an R2 value of 85.65011% and an MSE value of 161606.1.}, issn = {2339-2541}, pages = {512--521} doi = {10.14710/j.gauss.11.4.512-521}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/37864} }
Refworks Citation Data :
Article Metrics:
Last update:
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Gaussian and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Gaussian journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Jurnal Gaussian]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Rukun Santoso (Editor-in-Chief) Editorial Office of Jurnal GaussianDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: jurnalgaussian@gmail.com
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Visitor Number:
View statistics