Pemodelan Seasonal Autoregressive Integrated Moving Average  Untuk Memprediksi Jumlah Kasus Covid-19 di Padang

Widdya Rahmalina; Sari Puspita

doi:10.24198/jmi.v17.n1.32024.23-31

Pemodelan Seasonal Autoregressive Integrated Moving Average Untuk Memprediksi Jumlah Kasus Covid-19 di Padang

Widdya Rahmalina, Sari Puspita

Abstract

Padang city has been in the red zone (high risk) and orange zone (medium risk) against the transmission of the Covid-19 virus for several months. This is due to the lack of community discipline in complying with health protocols. The existence of the Andalas University Hospital Laboratory in Padang City which has the tools to issue the SWAB test results also results in data being obtained very quickly and data collection is more accurate. To predict the number of new cases of Covid-19 patients, research on forecasting is necessary. One method that can be used is the Seasonal Autoregressive Integrated Moving Average method or abbreviated as SARIMA. This method was chosen because the data shows a weekly seasonal pattern. The data used are daily data from 2 August 2020 to 6 January 2021 obtained from the Padang City Health Office . The results showed that the SARIMA (0,1,1) (0,1,1) 7 model is the best model with parameter estimates that are significantly different from zero, so that it fulfills the white noise assumption with a Means Squared Error value of 3.46731. Forecasting results for the next month show that cases of Covid-19 patients are still fluctuating, ranging from 20 to 66 people. For this reason, efforts from the local government of the City of Padang are needed in disciplining the community so that the conditions of Padang City can immediately turn into a green (safe) zone from Covid-19.

Keywords

Covid-19, forecasting, SARIMA, Padang.

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References

K. N. Azizah, “WHO Resmi Nyatakan Virus Corona COVID-19 sebagai Pandemi,” detikHealth, 2020. https://health.detik.com/berita-detikhealth/d-4935355/who-resmi-nyatakan-virus-corona-covid-19-sebagai-pandemi (accessed Jan. 06, 2021).

N. S. Nagita, “Corona Dunia Tembus 70 Juta Kasus, Indonesia Kini Peringkat Berapa?,” 2020. https://health.detik.com/berita-detikhealth/d-5291143/corona-dunia-tembus-70-juta-kasus-indonesia-kini-peringkat-berapa (accessed Jan. 06, 2021).

N. F. Shalihah, “10 Provinsi dengan Penambahan Kasus Covid-19 Terbanyak 4 Bulan Terakhir , Mana Saja ?,” Kompas.com, 2021. https://www.kompas.com/tren/read/2021/01/08/160500465/10-provinsi-dengan-penambahan-kasus-covid-19-terbanyak-4-bulan-terakhir (accessed Jan. 06, 2021).

Redaksi, “Padang Masuk 12 Besar Nasional Penyumbang Kasus Aktif Covid-19.” https://langgam.id/padang-masuk-12-besar-nasional-penyumbang-kasus-aktif-covid-19/ (accessed Jan. 06, 2021).

A. U. Ukhra, “Pemodelan Dan Peramalan Data Deret Waktu Dengan Metode Seasonal Arima,” J. Mat. UNAND, vol. 3, no. 3, p. 59, 2014, doi: 10.25077/jmu.3.3.59-67.2014.

A. Wibowo, “Model Peramalan Indeks Harga Konsumen Kota Palangka Raya Menggunakan Seasonal ARIMA (SARIMA),” Matematika, vol. 17, no. 2, pp. 17–24, 2018, doi: 10.29313/jmtm.v17i2.3981.

S. Makridakis, Steven Wheelwright, and R. J. Hyndman, Forecasting: Methods and Application. New York: John Wiley & Sons, 1998.

A. K. Rachmawati, “Peramalan Penyebaran Jumlah Kasus Covid19 Provinsi Jawa Tengah dengan Metode ARIMA,” Zeta - Math J., vol. 6, no. 1, pp. 11–16, 2020, doi: 10.31102/zeta.2021.6.1.11-16.

N. L. Sari, “Analisis Perbandingan Performa Metode Simple Moving Average dan Exponential Moving Average untuk Peramalan Jumlah Penderita Covid-19,” Indones. J. Data Sci., no. Vol. 1 No. 3 (2020): Indonesian Journal of Data and Science, 2020.

Dinkes Padang, “Situasi Terkini Perkembangan Kasus Coronavirus Disease (COVID-19) di Kota Padang_06 Januari 2021 Update Pukul 13.00 WIB,” 2021. https://dinkes.padang.go.id/situasi-terkini-perkembangan-kasus-coronavirus-disease-covid-19-di-kota-padang_06-januari-2021-update-pukul-1300-wib (accessed Jan. 06, 2021).

W. Rahmalina and Novreta, “Peramalan Indeks Kekeringan Kelayang Menggunakan Metode Sarima dan SPI,” Potensi J. Sipil Politek., vol. 22, no. 1, pp. 64–75, 2020, doi: 10.35313/potensi.v22i1.1824.

R. A. Yaffee and M. McGee, An Introduction to Time Series Analysis and Forecasting: With Applications of SAS®and SPSS®. Elsevier Science, 2000.

N. Lestari, Nofinda; Wahyuningsih, “Peramalan Kunjungan Wisata dengan Pendekketan Model SARIMA (Studi kasus : Kusuma Agrowisata),” J. Sains dan Seni ITS, vol. 1, no. Vol 1, No 1 (2012): Jurnal Sains dan Seni ITS (ISSN 2301-928X), pp. A29–A33, 2012, [Online]. Available: http://ejurnal.its.ac.id/index.php/sains_seni/article/view/1010/675.

H. Hadijah, “Peramalan Operasional Reservasi dengan Program Minitab menggunakan Pendekatan Arima PT Surindo Andalan,” The Winners, vol. 14, no. 1, p. 13, 2013, doi: 10.21512/tw.v14i1.640.

DOI: https://doi.org/10.24198/jmi.v17.n1.32024.23-31