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Abstract
The use of contraceptives, especially Long-Term Contraceptive Methods (MKJP), plays a central role in birth control efforts and family planning. Time series analysis has become a very effective method for identifying and predicting patterns in sequential data such as MKJP usage data. The data used is monthly data on the number of users of MKJP contraceptives (IUD, MOW, MOP and Implant) for the period January 2012 to 2012. December 2023. The aim of this research is to obtain comparative results of the accuracy of forecasting models using the Autoregressive Integrated Moving Average (ARIMA) and Prophet methods and to obtain projected results of MKJP contraceptive users in Bengkulu Province in the coming year. The results obtained overall, the ARIMA model is the best model for forecasting because it has mean absolute percentage error MAPE and root mean square error RMSE values, namely the ARIMA model (0,1,1). The forecast results for the number of MKJP contraceptive users (IUD, MOW, MOP and Implant) in 2024 tend to show a decreasing trend in May 2024 and an increasing trend in March 2024. For IUD contraception, it is known that the number of active family planning (PA) users is the lowest. was May 2024 with a total of 5593 participants, while the highest PA occurred in March 2024, namely 16742 participants. Then for MOW contraception, the lowest number of PAs was in May 2024, amounting to 6028 participants, while the highest PA was in March 2024, amounting to 8417 participants. Furthermore, for MOP contraception, it is known that the lowest number of PAs was in December 2024, amounting to 79 participants, while the peak PA occurred in March 2024, namely 614 participants. And finally, for IMPLANT contraception, it is known that the lowest number of PA was in May 2024, amounting to 26,771 participants, while the highest PA occurred in March 2024, namely 50,957 participants.
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Copyright (c) 2024 Agus Veriyansah Dalimunthe
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References
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References
Yenidoğan, I., Çayır, A., Kozan, O., Dag, T., & Arslan, Ç. (2018). Bitcoin Forecasting Using ARIMA and PROPHET. 2018 3rd International Conference on Computer Science and Engineering (UBMK), 621-624.
Damodar N., Gujarati dan Dawn C. Porter. (2009). Basic Econometric 5th Edition. McGraw –Hill: New York.
Adhikari, R. & Agrawal, R.K. (2013). An Introductory Study in Time Series Modeling and Forecasting. First Edition, KAP LAMBERT Academic Publishing, Saarbrucken.
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2008). Forecasting methods and applications. John wiley & sons.
Hanke, J. E., Reitsch, A. G., & Wichern, D. W. (2003). Peramalan Bisnis (Edisi Ketujuh) [Business Forecasting]. (D. Anantanur, Alih Bahasa). Jakarta: PT. Prenhallindo.
Primaditya, Vincentius Iwan. (2014). Pemodelan Box-Jenkins (ARIMA) untuk Peramalan Indeks Harga Saham Gabunga. Magister Manajemen Teknologi Institut Teknologi Sepuluh November.
William W.S. Wei. 2006. Time series Analysis Univariate and Multivariate Methods, Addison-Wesley Publishing Company.
Aswi dan Sukarna, (2006). Analisis Deret Waktu Teori dan Aplikasi, Makassar: Andira Publisher
Box, George E.P., et. al., (2008). Time Series Analysis: Forecasting and Control. Fourth Edition, A John Wiley & Sons, inc., PublisherBO
Wie, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods Second Edition. Pearson Education, Inc., SFB 373(Chapter 5), 837–900.
Nachrowi, D. N., & Usman, H. (2006). Ekonometrika. Jakarta: LPFEUI.
Cryer, J. D., & Chan, Kung-Sik. (2008). Time Series Analysis with Applications in R Second Edition. New York: Springer.
Cryer, J. D. (1986). Time series analysis (Vol. 286). Boston: Duxbury Press.
Taylor, S. J., & Letham, B. (2017). Forecasting at scale.