Forecasting the Number of Tuberculosis Patients Visiting Mitra Sehat Clinic With ARIMA Method
DOI:
https://doi.org/10.26821/IJSHRE.12.9.2024.120905Keywords:
Arima, Forecasting, Patient Visits, Tuberculosis, Minitab, Sukoharjo IndonesiaAbstract
Tuberculosis (TB) remains a significant public health concern in many parts of the world, including Indonesia. Accurate forecasting of the number of TB patients is crucial for health clinics to prepare resources and optimize patient care. This study aims to forecast the number of tuberculosis patients visiting the Mitra Sehat Clinic using the AutoRegressive Integrated Moving Average (ARIMA) method. The ARIMA model is widely used for time series forecasting due to its ability to capture the dynamics of various types of data. Data on patient visits to the clinic over a specified period were collected and analyzed to develop an appropriate ARIMA model. The steps for using the ARIMA method are using data from the required sample from patients visiting with Tuberculosis diagnosed in January 2021 – August 2024, determining the type of time series data pattern, then conducting a stationarity test, determining the ARIMA model, calculating and analyzing the accuracy of the model used, then forecasting the number of outpatient visits. Based on the calculation, the best ARIMA model for this forecasting is (1, 0, 1) with an error value of 42,0276. The results indicate that the ARIMA model can provide reliable forecasts, assisting healthcare providers in managing resources and improving service delivery for TB patients. This research contributes to the strategic planning of health services and highlights the importance of predictive analytics in healthcare.
References
. World Health Organization (WHO). (2022). Global Tuberculosis Report.
. Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
. Zhu, X., et al. (2018). Influenza-like illness prediction using a local epidemic model with ARIMA. Journal of Infectious Diseases.
. Benvenuto, D., et al. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in Brief
. Indonesian Health Social Security Agency (BPJS), “Daftar Kunjungan Peserta Sakit.” Online available from https://pcarejkn.bpjs-kesehatan.go.id/eclaim/ViewKunjunganSakitByPpkTgl (Accessed on 1 February 2022)
. Montgomery D. C., Jennings C. L., Kulahci M., "Time Series Analysis And Forecasting: Statistics Background For Forecasting," 2nd ed, John Wiley & Sons Inc., 2015, pp. 25–41.
. Fadhilah Abd. Razak, Mahendran Shitan, Amir H. Hashim, Izham Z. Abidin, "Load Forecasting Using Time Series Models." Jurnal Kejuruteraan, pp. 53-62, 2009. DOI : dx.doi.org/10.17576/jkukm-2009-21-06
. Nany Salwa, Nidya Tatsara, Ridha Amalia, Aja Fatimah Zohra, “Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average).”, Journal of Data Analysis, Vol. 1, No. 1, pp. 21–31, 2018. DOI: https://doi.org/10.24815/jda.v1i1.11874
. Saleh I Alzahrani, Ibrahim A Aljamaan, Ebrahim A Al-Fakih, "Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions." J Infect Public Health, National Library of Medicine, pp. 914-919, 2020. DOI : 10.1016/j.jiph.2020.06.001
. Hiteshi Tandon, Prabhat Ranjan, Tanmoy Chakraborty, Vandana Suhag, "Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future." arXiv Cornell University, 2020. DOI: https://doi.org/10.48550/arXiv.2004.07859
. Willian W.S. Wei, "Time Series Analysis, Univariate and Multivariate Methods," 2nd ed, Pearson Addison Wesley, 2006.
. Ridha Maya Faza Lubis1, Zakarias Situmorang, Rika Rosnelly, “Autoregressive Integrated Moving Average (ARIMA-Box Jenkins) Pada Peramalan Komoditas Cabai Merah di Indonesia”, Jurnal Media Informatika Budidarma, pp. 487-489, 2021. DOI: 10.30865/mib.v5i2.2927
. Muhammad Bintang Pamungkas, Arief Wibowo, “Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Kasus Dbd Di Provinsi Jawa Timur”, The Indonesian Journal of Public Health, pp. 183-186, 2018. DOI: https://doi.org/10.20473/ijph.v13i2.2018.183-196
. Susilo Hariyanto, "Forecasting the Number of Outpatient Patient Visits Using ARIMA at Mitra Sehat Clinic: Case Study in Sukoharjo, Central Java Indonesia," Universal Journal of Public Health Vol. 10(5), pp. 482 – 491, 2022. DOI: 10.13189/ujph.2022.100506
. Susilo Hariyanto, " Forecasting the Number of Patients Visits with Diabetes Mellitus Diagnosed Using ARIMA at Mitra Sehat Clinic Sukoharjo Indonesia," International Journal of Applied Engineering Research (IJAER) Volume 18, Number 4 (2023), pp: 256-261.
