TY - JOUR
T1 - Forecasting tuberculosis morbidity rate in Indonesia using autoregressive integrated moving average (ARIMA) method
AU - Lesmono, W. D.
AU - Mardiyati, S.
AU - Lestari, D.
AU - Zili, A. H.A.
N1 - Funding Information:
This work was financially supported by Universitas Indonesia under research grant PITTA with grant contract number NKB-0668/UN2.R3.1/HKP.05.00/2019.
Publisher Copyright:
© 2021 Journal of Physics: Conference Series.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/12
Y1 - 2021/1/12
N2 - Tuberculosis is a disease that can affect socio-economic development. Based on data from the World Health Organization, there were 810,918 tuberculosis cases in Indonesia, which is noted as the third-highest number of tuberculosis cases in Asia in 2016. Prevention and control of tuberculosis are of considerable importance, especially in the insurance field, to cover the cost of treatment, so an accurate model of tuberculosis morbidity is needed. The method used in forecasting the tuberculosis morbidity rate is Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is a time series method that is widely used to predict morbidity rates in the future. The data used in this study is the number of incidence morbidity tuberculosis rates that occurred in Indonesia from 2000 to 2017, which is obtained from the World Bank. The results showed that ARIMA (1, 2, 0) is the best and very accurate model to forecast the morbidity rate in Indonesia from 2018 to 2027, with the mean absolute percentage error (MAPE) is 0.1682 % and Akaike Information Criterion (AIC) values is -181.0120. The results of forecasting tuberculosis morbidity rate are expected to help insurance companies in determining the amount of premium paid by customers who suffer tuberculosis diseases.
AB - Tuberculosis is a disease that can affect socio-economic development. Based on data from the World Health Organization, there were 810,918 tuberculosis cases in Indonesia, which is noted as the third-highest number of tuberculosis cases in Asia in 2016. Prevention and control of tuberculosis are of considerable importance, especially in the insurance field, to cover the cost of treatment, so an accurate model of tuberculosis morbidity is needed. The method used in forecasting the tuberculosis morbidity rate is Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is a time series method that is widely used to predict morbidity rates in the future. The data used in this study is the number of incidence morbidity tuberculosis rates that occurred in Indonesia from 2000 to 2017, which is obtained from the World Bank. The results showed that ARIMA (1, 2, 0) is the best and very accurate model to forecast the morbidity rate in Indonesia from 2018 to 2027, with the mean absolute percentage error (MAPE) is 0.1682 % and Akaike Information Criterion (AIC) values is -181.0120. The results of forecasting tuberculosis morbidity rate are expected to help insurance companies in determining the amount of premium paid by customers who suffer tuberculosis diseases.
KW - MAPE
KW - Premiums
KW - Stationarity
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85100751372&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1725/1/012031
DO - 10.1088/1742-6596/1725/1/012031
M3 - Conference article
AN - SCOPUS:85100751372
SN - 1742-6588
VL - 1725
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012031
T2 - 2nd Basic and Applied Sciences Interdisciplinary Conference 2018, BASIC 2018
Y2 - 3 August 2018 through 4 August 2018
ER -