Model Time Series untuk Prediksi Jumlah Kasus Infeksi Coronavirus (Covid-19) di Sulawesi Selatan

Asrirawan Asrirawan, Andi Seppewali, Nurul Fitriyani

Abstract


Since it was declared a pandemic outbreak, the COVID 19 virus has become one of the main focuses of countries in the world in efforts to prevent the spread of the virus, including Indonesia. The areas of greatest severity in Indonesia include Jakarta, East Java, West Java and South Sulawesi. South Sulawesi Province is recorded as the largest area exposed to the COVID 19 pandemic outside Java Island. Predicting the number of COVID 19 cases is an alternative in preventing the spread through making government policies based on predictive data. This article presents a predictive model for the number of COVID 19 cases based on the ARIMA, Holt Winters and Nonlinear Autoregressive Neural Network (NAR-NN) Model. The results of the analysis show that the ARIMA Model (1,1,1) has a better level of prediction accuracy than the HW and NAR-NN models based on the MAPE criteria. Meanwhile, for the RMSE, MAE and MPE criteria, the NAR-NN model is better than others.

References


V. Papastefanopoulos, P. Linardatos, and S. Kotsiantis, 2020, “COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population”, Appl. Sci. 2020, 10(11), 3880; https://doi.org/10.3390/app10113880, 2020.

M. F. Khan, and R. Gupta, “ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India”, Journ. Safety Science and Resilience, 2020, 1(12), https://doi.org/10.1016/j.jnlssr.2020.06.007. 2020.

K. Abdulmajeed, M. Adelekeb, and L. Popoola, “Online Forecasting of COVID-19 Cases in Nigeria using Limited Data”, Data in Brief, 30, https://doi.org/10.1016/j.dib.2020.105683. 2020.

W.W.S. Wei, “Time Series Analysis, Univariate and Multivariate Methods”, 2¬¬-nd ed. United States: Addison-Wesley Publishing Company. 2006.

J. D. Cryer, “Time Series Analysis”, Boston: PWS-KENT Publishing Company. 1986.

Asrirawan, “Simulasi Perbandingan Metode Peramalan Model Generalized Seasonal Autoregressive Integrated Moving Average (GSARIMA) Dengan Seasonal Autoregressive Integrated Moving Average (SARIMA)”, Jurn. Dinamika, 1(6), Universitas Cokroaminoto Palopo. 2015.

Haslina, Hasmah, K. W. Fitriani, M. Asbar, dan Asrirawan, “Penerapan Metode ARIMA (Autoregressive Integrated Moving Average) Box Jenkins Untuk Memprediksi Pertambahan Jumlah Penduduk Tansmigran (Jawa Dan Bali) Di Kecamatan Sukamaju, Kabupaten Luwu Utara Propinsi Sulawesi Selatan”, Jurn.Dinamika, 1(9), Universitas Cokroaminoto Palopo. 2018.

P. J. Brockwell and R. A. Davis, “Introduction to Time Series and Forecasting, 3rd ed”. New York: Springer-Verlag New York. 2016

G. Benrhmach, Namir, A. Namir and J. Bouyaghroumni, “Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series”, Journal of Applied Mathematics Volume 2020 (6), https://doi.org/10.1155/2020/5057801. 2020




DOI: https://doi.org/10.24252/msa.v8i2.17427

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