Peramalan Prevalensi Tuberkulosis Provinsi Jawa Barat Menggunakan Model ARIMA

  • Venita Syavera Universitas Pertahanan Indonesia
  • Diara Winanda Universitas Pertahanan Indonesia
Keywords: tuberculosis, time series analysis, ARIMA, forecasting, West Java

Abstract

Tuberculosis (TB) remains a major public health problem in Indonesia, with West Java Province contributing the largest share of national cases. Understanding temporal dynamics of TB cases is essential to support disease control planning. This study aims to analyze the temporal pattern and short-term forecasting of tuberculosis cases in West Java Province using the Autoregressive Integrated Moving Average (ARIMA) model. Secondary time-series data of TB cases from all districts and cities in West Java during the period 2019-2023 were used and aggregated at the provincial level. Model identification was conducted through stationarity testing and analysis of autocorrelation and partial autocorrelation functions. The best-performing model was selected based on information criteria and residual diagnostics. The results indicate that ARIMA(1,1,1) is the most suitable model for representing the temporal dynamics of TB cases. Forecasting results for the next six months show a relatively stable trend without extreme fluctuations, although the predicted number of cases remains high, particularly in densely populated urban areas such as Bogor Regency, Bandung City, and Depok City. These findings demonstrate that ARIMA provides a simple yet effective approach for short-term forecasting of TB cases and can support provincial-level planning and decision-making in tuberculosis control programs.

References

Ab Rashid, M. A., Ahmad Zaki, R., Wan Mahiyuddin, W. R., & Yahya, A. (2023). Forecasting New Tuberculosis Cases in Malaysia: A Time-Series Study Using the Autoregressive Integrated Moving Average (ARIMA) Model. Cureus, 15(9). https://doi.org/10.7759/CUREUS.44676
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). JOURNAL OF TIME SERIES ANALYSIS BOOK REVIEW TIME SERIES ANALYSIS: FORECASTING AND CONTROL, 5TH EDITION, by. J. Time. Ser. Anal, 37, 709–711. Retrieved from https://www.wiley.com/en-us/Time+Series+Analysis%3A+Forecasting+and+Control%2C+5th+Edition-p-9781118675021
Chen, S., Wang, X., Zhao, J., Zhang, Y., & Kan, X. (2022). Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022. Infection and Drug Resistance, 15, 3503. https://doi.org/10.2147/IDR.S367528
Chenqi, Y., Ruibai, W., Haican, L., Yi, J., MacHao, L., Sfuqteng, Y., … Weufing, R. (2019). [Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018 to 2019]. Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi, 40(6), 633–637. https://doi.org/10.3760/CMA.J.ISSN.0254-6450.2019.06.006
Coles, S. G., & Tawn, J. A. (1994). Statistical Methods for Multivariate Extremes: An Application to Structural Design. Applied Statistics, 43(1), 1. https://doi.org/10.2307/2986112
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. Retrieved November 7, 2025, from https://books.google.co.id/books?hl=id&lr=&id=_bBhDwAAQBAJ&oi=fnd&pg=PA7&dq=Forecasting:+Principles+and+Practice&ots=TjkXxiZKGG&sig=gAdoZLBPYuHF4YesJI0HWCM849Y&redir_esc=y#v=onepage&q=Forecasting%3A Principles and Practice&f=false
Lönnroth, K., Jaramillo, E., & Williams, B. (2009). Drivers of tuberculosis epidemics: the role of risk factors and social determinantsQ1Social science & medicine; H-Index: 255 SJR: Q1 CORE: NA ABDC: NA FT50: NA 21A24Social science & medicine; H-Index: 255 VHB: NA FNEGE: 2 CoNRS: 1 HCERE: A …. ElsevierK Lönnroth, E Jaramillo, BG Williams, C Dye, M RaviglioneSocial Science & Medicine, 2009•Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0277953609002111
Pai, M., Behr, M. A., Dowdy, D., Dheda, K., Divangahi, M., Boehme, C. C., … Raviglione, M. (2016). Tuberculosis. Nature Reviews Disease Primers 2016 2:1, 2(1), 16076-. https://doi.org/10.1038/nrdp.2016.76
Puspita, T., Suryatma, A., Simarmata, O. S., Veridona, G., Lestary, H., Athena, A., … Pakasi, T. T. (2021). Spatial variation of tuberculosis risk in Indonesia 2010-2019. Health Science Journal of Indonesia, 12(2), 104–110. https://doi.org/10.22435/HSJI.V12I2.5467
World Health Organization. (2022). Annual Report of Tuberculosis. Annual Global TB Report of WHO, 8(1), 1–68. Retrieved from https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022%0Ahttps://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022#:~:text=context of global...-,Download,-Read More%0Ahtt
World Health Organization. (2024). Global Tuberculosis Report 2024. Geneva: WHO; 2024. Licence: CC BY-NC-SA 3.0 IGO. Global Tuberculosis Report, 68. Retrieved from https://www.who.int/publications/i/item/9789240101531
Published
2026-03-30
Section
Articles