Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

Desri Kristina Silalahi

doi:10.31764/jtam.v4i1.1888

Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

Desri Kristina Silalahi

Abstract

The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)₄. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.

Keywords

Forecasting; Poverty Data; SARIMA Method; Government Policy.

Full Text:

Download [PDF]

References

Chang, P.-C., Wang, Y.-W., & Liu, C.-H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32, 86–96. https://doi.org/doi:10.1016/j.eswa.2005.11.021

Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1608207113

Ekananda, M. (2014). Analisis Data Time Series Untuk Penelitian Ekonomi, Manajemen, Dan Akutansi. Mitra Wacana.

Hermawan. (2011). Perbandingan metode Box-Jenkins dan Holt-Winters dalam memprediksi anomali Olr Pentad di kawasan barat Indonesia. Jurnal Sains Dirgantara, 9(1), 27–34.

Hillmer, S. C., & Wei, W. W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods. Addison Wesley Publishing Company, Inc. https://doi.org/10.2307/2289741

Kaur, H., & Ahuja, S. (2019). SARIMA modelling for forecasting the electricity consumption of a health care building. International Journal of Innovative Technology and Exploring Engineering, 8(12), 2795–2799. https://doi.org/10.35940/ijitee.L2575.1081219

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/https://doi.org/10.1016/j.ijforecast.2015.12.003

Kumila, A., Sholihah, B., Evizia, Safitri, N., & Fitri, S. (2019). Perbandingan metode Moving Average dan metode Naïve dalam peramalan data kemiskinan. Jurnal Teori Dan Aplikasi Matematika (JTAM), 3(1), 65–73. https://doi.org/https://doi.org/10.31764/jtam.v3i1.764

Malkina-Pykh, I. G., & Pykh, Y. A. (2019). Some notes on the application of R-squared for evaluation the goodness-of-fit of nonlinear regression models. 8th International Nonlinear Science Conference, 1–7. https://doi.org/10.13140/RG.2.2.35129.21607

Matondang, E. (2017). Finding Out the Potency of Nusa Tenggara Timur in Poverty Allevation: The Effect of Local Government’s Policy. Jurnal Bina Praja, 9(2), 231–242. https://doi.org/10.21787/jbp.09.2017.231-242

Ngo, T. H. D. (2013). The Box-Jenkins Methodology for Time Series Models. Proceedings of the SAS Global Forum 2013 Conference.

Octora, M., & Kuntoro. (2013). Perbandingan metode ARIMA (Box Jenkins) dan metode Winter dalam peramalan jumlah kasus demam berdarah dengue. Jurnal Biometrika Dan Kependudukan, 2(1), 88–98.

Patowary, A. N., & Barman, M. P. (2017). Development of a SARIMA model to forecast Tuberculosis detection rate in the Dibrugarh District of Assam, India. International Journal of Advanced Research in Computer Science, 8(7), 984–988. https://doi.org/http://dx.doi.org/10.26483/ijarcs.v8i7.4424

Pongdatu, G. A. N., & Putra, Y. H. (2018). Seasonal Time Series Forecasting using SARIMA and Holt Winter’s Exponential Smoothing. IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/407/1/012153

Rahmasari, U., S, E. H., Jannah, M., Pathullaili, Kurnia, L., & Satria, A. (2019). ARDL method: forecasting data kemiskinan di NTB. Jurnal Teori Dan Aplikasi Matematika (JTAM), 3(1), 52–57. https://doi.org/https://doi.org/10.31764/jtam.v3i1.767

Seymour, L., Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting. Springer. https://doi.org/10.2307/2965440

Silastri, N., Iyan, R., & Sari, L. (2017). Pengaruh Jumlah Penduduk dan Pendapatan Domestik Regional Bruto (PDRB) terhadap Kemiskinan di Kabupaten Kuantan Singingi. Jurnal Online Mahasiswa Fakultas Ekonomi Universitas Riau, 4(1), 105–117.

Tadesse, K. B., & Dinka, M. O. (2017). Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa. Journal of Water and Land Development, 35(X–XII), 229–236. https://doi.org/10.1515/jwld-2017-0088

Ukhra, A. U. (2014). Pemodelan dan peramalan data deret waktu dengan metode SEASONAL ARIMA. Jurnal Matematika UNAND, 3(3), 59–67.

W. Gikungu, S. (2015). Forecasting inflation rate in Kenya using SARIMA model. American Journal of Theoretical and Applied Statistics, 4(1), 15–18. https://doi.org/10.11648/j.ajtas.20150401.13

Wibowo, A. (2018). Model peramalan indeks harga konsumen kota Palangka Raya menggunakan Seasonal ARIMA (SARIMA). Jurnal Matematika, 17(2), 17–24.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079

DOI: https://doi.org/10.31764/jtam.v4i1.1888