Peramalan jumlah pasien dengan diagnosa acute upper respiratory infection menggunakan metode autoregressive integrated moving average (ARIMA) pada Klinik Pratama Mitra Sehat

Authors

  • Berlian Harry Saputra Departemen Matematika Fakultas Sains dan Matematika Universitas Diponegoro
  • Shafa Departemen Matematika Fakultas Sains dan Matematika Universitas Diponegoro
  • Annisa Departemen Matematika Fakultas Sains dan Matematika Universitas Diponegoro
  • Andrean Departemen Matematika Fakultas Sains dan Matematika Universitas Diponegoro
  • Anggiat Departemen Matematika Fakultas Sains dan Matematika Universitas Diponegoro

Keywords:

acute upper respiratory infections, ARIMA, forecasting

Abstract

Acute upper respiratory infection is the most common disease suffered by patients at the Pratama Mitra Sehat clinic. During the last 2.5 years, the number of patients suffering from acute upper respiratory infection has reached 12875 people. Some of the diseases included in this infection are colds, sinusitis, tonsillitis, and laryngitis. This type of disease can be transmitted through the air. The study will estimate the number of patients diagnosed with acute upper respiratory infections in the next few months. ARIMA or Autoregressive Integrated Moving Average is a forecasting method that will be used to predict the number of patients suffering from acute upper respiratory tract infections, so that in the future Pratama Mitra Sehat Clinic can prepare matters related to diagnosis. The steps for using the ARIMA method are using data from the required sample, determining the type of time series data pattern, conducting a stationarity test, determining the ARIMA model, calculating and analyzing the accuracy of the model used, then forecasting. The best ARIMA model for this forecasting based on the calculation is (2, 0, 1) with an error value 0.0389305.

References

Abd. Razak, F., Shitan, M., Hashim, A. H., & Z. Abidin, I. (2009). Load Forecasting Using Time Series Models. Jurnal Kejuruteraan.

Fauzannisa, R. A., Yasin, H., & Ispriyanti, D. (2015). Peramalan Harga Minyak Mentah Dunia Menggunakan Metode Radial Basis Function Neural Network. Jurnal Gaussian, 5, 193–202.

Fitriani, B., Ispriyanti, D., & Prahutama, A. (2015). Peramalan Beban Pemakaian Listrik Jawa Tengah Dan Daerah Istimewa Yogyakarta Dengan Menggunakan Hybrid Autoregresive Integrated Moving Average  Neural Network. Jurnal Gaussian, 4(4), 745–754.

Gunawan, R. M., Setiawati, Djamaludin, D., & Pribadi, T. (2020). Pendidikan Kesehatan Infeksi Saluran Pernafasan Akut (ISPA) Di Posyandu Anggrek 7 Gg. Mawar Kemiling Bandar Lampung. Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM), 3(1), 74–79.

Halodoc, R. (2019). Infeksi Saluran Pernapasan - Gejala, Penyebab, dan Cara Mengobati | Halodoc.com. Diakses pada April 24, 2021, dari https://www.halodoc.com/kesehatan/infeksi-saluran-pernapasan

Kemenkes RI. (2018). Laporan Riskesdas 2018 (Vol. 53). Jakarta: Badan Penelitian dan Pengembangan Kesehatan.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction Time Series Analysis and Forecasting (2nd ed.). New Jersey: John Wiley & Sons, Inc.

Putra, Y., & Wulandari, S. S. (2019). Faktor Penyebab Kejadian Ispa. Jurnal Kesehatan, 10(1), 37.

Raharja, A., Angraeni, W., & Aulia Vinarti, R. (2017). Penerapan Metode Exponential Smoothing Untuk Peramalan Penggunaan Waktu Telepon Di Pt.Telkomsel Divre3 Surabaya. Jurnal Sistem Informasi (SISFO), 59, 73.

Salwa, N., Tatsara, N., Amalia, R., & Zohra, A. F. (2018). Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average). Journal of Data Analysis, 1(1), 21–31.

Santoso, S. (2009a). Business Forecasting Metode Peramalan Bisnis Masa Kini dengan Minitab dan SPSS. Jakarta: Elex Media Komputindo.

Santoso, S. (2009b). Business Forecasting Metode Peramalan Bisnis Masa Kini dengan Minitab dan SPSS. Jakarta: Elex Media Komputindo.

Ünüvar, E., Yildiz, İ., Kiliç, A., Selvi̇ Aslan, S., Çakal, B., Toprak, S., Badur, S., et al. (2009). Viral Etiology and Symptoms of Acute Upper Respiratory Tract Infections in Children, 39(1), 29–35.

Downloads

Published

2021-08-22

How to Cite

Saputra, B. H., Afifah, S. N., Indahsari, A., Yonathan, A., & Simanjuntak, A. W. J. (2021). Peramalan jumlah pasien dengan diagnosa acute upper respiratory infection menggunakan metode autoregressive integrated moving average (ARIMA) pada Klinik Pratama Mitra Sehat. Prosiding Seminar Nasional Matematika Dan Pendidikan Matematika, 6, 113–120. Retrieved from https://conference.upgris.ac.id/index.php/senatik/article/view/1832

Issue

Section

Articles