DOI: https://doi.org/10.14710/j.gauss.12.2.304-313

PENERAPAN MODEL LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) UNTUK PERAMALAN KASUS COVID-19 DI INDONESIA

*Lutfi Ardining Tyas  -  Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember, Jl. Kalimantan No. 37 Kampus Tegalboto, Jember, Indonesia 68121, Indonesia
I Made Tirta  -  Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember, Indonesia
Yuliani Setia Dewi  -  Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember, Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Forecasting is about predicting the future based on historical data and any information that might affects the forecasts. This article applies the LSSVM model to forecast Covid-19 cases in Indonesia. The purpose of this study is to find out how the LSSVM model applied and the model performances for forecasting Covid-19 cases in Indonesia, using time series data and the factors that influence it, as input features. The factor data used in this study are mobility data and daily fully vaccinated data. The research has three main objectives; first, calculate the correlation between confirmed cases data and past data (lag) of mobility and vaccination. Second, is the selection of input features based on the highest correlation coefficient value of each variable. Third, do LSSVM modeling and Covid-19 case forecasting with the optimal model. RBF kernel and grid-search algorithm with 10-fold cross-validation are used to tune model parameters. The results show that the LSSVM model provides good performance for Covid-19 forecasting and the optimal LSSVM model for forecasting Covid-19 cases in Indonesia is using time lag 14 for the mobility factor and time lag 24 for the vaccination factor.

Fulltext View|Download
Keywords: lssvm; forecasting; time series; covid-19
Funding: I Made Tirta, Jurusan Matematika, Universitas Jember

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
PEMODELAN DAN PREDIKSI HARGA SAHAM PERUSAHAAN FAST MOVING CUSTOMER GOODS MENGGUNAKAN VECTOR AUTOREGRESSIVE WITH EXOGENOUS VARIABLES (VARX) K-NEAREST NEIGHBOR DENGAN ADAPTIVE BOOSTING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE UNTUK KLASIFIKASI DATA TIDAK SEIMBANG ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL INDEKS TUNGGAL DAN PENGUKURAN VALUE AT RISK DENGAN SIMULASI MONTE CARLO (Studi Kasus: Exchange Traded Fund di Bursa Efek Indonesia Periode Januari 2021 – Juni 2022) More recent articles
  1. Berrar, D. (2018) ‘Cross-validation’, Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1–3, pp. 542–545. doi: 10.1016/B978-0-12-809633-8.20349-X
  2. Box, G.E. et al. (2015) Time series analysis: forecasting and control. John Wiley & Sons
  3. Espinoza, M. et al. (2008) ‘Time Series Prediction using LS-SVMs’, European Symposium on Time Series Prediction, pp. 159--168
  4. Google LCC. 2022. COVID-19 Community Mobility Reports, 2022. https://www.google.com/covid19/mobility/. Diakses : 10 Maret 2022
  5. Hyndman, R.J. and Athanasopoulos, G. (2018) Forecasting: principles and practice. OTexts
  6. Johns Hopkins University. Novel Coronavirus (COVID-19) Cases Data. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases. Diakses:10 Maret 2022
  7. Liashchynskyi, Petro and Liashchynskyi, Pavlo (2019) ‘Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS’, ArXiv [Preprint], (December 2019)
  8. Polack, F.P. et al. (2020) ‘Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine’, New England Journal of Medicine, 383(27), pp. 2603–2615. doi: 10.1056/nejmoa2034577
  9. Snell, J. and Sprent, P. (1995) Applied Nonparametric Statistical Methods., Journal of the Royal Statistical Society. Series A (Statistics in Society). doi: 10.2307/2983315
  10. Susilo, A. et al. (2020) ‘Coronavirus Disease 2019: Tinjauan Literatur Terkini’, Jurnal Penyakit Dalam Indonesia, 7(1), p. 45. doi: 10.7454/jpdi.v7i1.415
  11. Suykens, J.A.K. and Vandewalle, J. (2002) Least squares support vector machines. World Scientific Publishing Co. Pte. Ltd
  12. The Humanitarian Data Exchange. Coronavirus (COVID-19) Vaccinations. https://data.humdata.org/dataset/covid-19-vaccinations. Diakses:10 April 2022
  13. Unit Pelayanan Kesehatan Kemenkes. (2021) 4 Manfaat Vaksin Covid-19 yang Wajib Diketahui. https://upk.kemkes.go.id/new/4-manfaat-vaksin-covid-19-yang-wajib-diketahui. Diakses : 19 April 2022
  14. Vogt, W. and Johnson, R. (2015) Correlation and Regression Analysis, The Statistical, Economic and Social Research and Training Centre for Islamic Countries (SESRIC). doi: 10.4135/9781446286104
  15. Vu, G.X.. and Zhong, Z.. (2018) ‘Forecasting Air Passengers of Changi Airport Based on Seasonal Decomposition and an LSSVM Model’, Review of Information Engineering and Applications, 5(1), pp. 12–30. doi: 10.18488/journal.79.2018.51.12.30
  16. World Health Organization (2020) ‘COVID-19 Situation Report 73’, World Health Organization, 2019(March), p. 2633. doi: 10.1056/NEJMoa2001316.4
  17. Zhang, W., Li, C. and Zhong, B. (2009) ‘LSSVM parameters optimizing and non-linear system prediction based on cross validation’, 5th International Conference on Natural Computation, ICNC 2009, 1(3), pp. 531–535. doi: 10.1109/ICNC.2009.26

Last update:

No citation recorded.

Last update:

No citation recorded.