Forecasting the Poverty Rates using Holt’s Exponential Smoothing
Keywords:
Forecasting, Holt’s Exponential Smoothing, Root Mean Square Error, Mean Square Error, Sum Squared Error
Abstract
As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.
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[2] C. Lakner, D. G. Mahler, M. Negre, and E. B. Prydz, “How much does reducing inequality matter for global
poverty?” The Journal of Economic Inequality, vol. 20, no. 3, pp. 559–585, Sep. 2022. [Online]. Available:
https://link.springer.com/10.1007/s10888-021-09510-w
[3] K. D. Hartomo, S. Yulianto, and A. Valentina, “A New Model of Poverty Index Prediction Using Triple Exponential Smoothing
Method,” in 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE).
Semarang, Indonesia: IEEE, Sep. 2020, pp. 76–79. [Online]. Available: https://ieeexplore.ieee.org/document/9239205/
[4] World Bank, Indonesia Poverty Assessment - Pathways towards Economic Security. World Bank, May 2023. [Online].
Available: http://elibrary.worldbank.org/doi/book/10.1596/39799
[5] G. Hurulle, S. Bamaramannage, and H. Galpaya, “Data for Poverty Measurement,” LIRNEasia, Sep. 2023. [Online]. Available:
https://lirneasia.net/2023/09/data-for-poverty-measurement
[6] F. Riandari, H. T. Sihotang, and H. Husain, “Forecasting the Number of Students in Multiple Linear Regressions,” MATRIK :
Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp. 249–256, Mar. 2022. [Online]. Available:
https://journal.universitasbumigora.ac.id/index.php/matrik/article/view/1348
[7] D. Rajapaksha, C. Bergmeir, and R. J. Hyndman, “LoMEF: A framework to produce local explanations for global model
time series forecasts,” International Journal of Forecasting, vol. 39, no. 3, pp. 1424–1447, Jul. 2023. [Online]. Available:
https://linkinghub.elsevier.com/retrieve/pii/S0169207022000978
[8] A. Wanto, N. L. W. S. R. Ginantra, S. Hendraputra, I. O. Kirana, and A. R. Damanik, “Optimization of
Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model,” MATRIK : Jurnal Manajemen,
Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 1, pp. 51–82, Nov. 2022, number: 1. [Online]. Available:
https://journal.universitasbumigora.ac.id/index.php/matrik/article/view/1826
[9] V. N. Markova, K. I. Alekseeva, A. B. Neustroeva, and E. V. Potravnaya, “Analysis and Forecast of the Poverty Rate in the
Arctic Zone of the Republic of Sakha (Yakutia),” Studies on Russian Economic Development, vol. 32, no. 4, pp. 415–423, Jul.
2021. [Online]. Available: https://link.springer.com/10.1134/S1075700721040109
[10] B. P. S. D. I. Yogyakarta, Survei Sosial Ekonomi Nasional, 2022.
[11] C. L. Karmaker, “Determination of Optimum Smoothing Constant of Single Exponential Smoothing Method: A Case
Study,” International Journal of Research in Industrial Engineering, no. Online First, Aug. 2017. [Online]. Available:
https://doi.org/10.22105/riej.2017.49603
[12] P. Montero-Manso and R. J. Hyndman, “Principles and algorithms for forecasting groups of time series: Locality
and globality,” International Journal of Forecasting, vol. 37, no. 4, pp. 1632–1653, Oct. 2021. [Online]. Available:
https://linkinghub.elsevier.com/retrieve/pii/S0169207021000558
[13] R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, 1st ed. Lexington, Ky: Otexts, 2014.
[14] L. F. Laurente Blanco and R. W. Machaca Hancco, “Modelamiento y proyeccin de la demanda de turismo internacional
en Puno-Per,” Revista Brasileira de Pesquisa em Turismo, vol. 14, no. 1, pp. 34–55, Jan. 2020. [Online]. Available:
https://rbtur.org.br/rbtur/article/view/1606
[15] A. S. Ahmar, F. Fitmayanti, and R. Ruliana, “Modeling of inflation cases in South Sulawesi Province using single exponential
smoothing and double exponential smoothing methods,” Quality & Quantity, vol. 56, no. 1, pp. 227–237, Feb. 2022. [Online].
Available: https://link.springer.com/10.1007/s11135-021-01132-8
[16] J. Vimala and A. Nugroho, “Forecasting Penjualan Obat Menggunakan Metode Single, Double, dan Triple Exponential
Smoothing (Studi Kasus: Apotek Mandiri Medika),” IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi,
vol. 1, no. 2, pp. 90–99, Jun. 2022. [Online]. Available: https://ejournal.uksw.edu/itexplore/article/view/6289
[17] M. Mirdaolivia and A. Amelia, “Metode Exponential Smoothing untuk Forecasting Jumlah Penduduk Miskin
di Kota Langsa,” JURNAL GAMMA-PI, vol. 3, no. 1, pp. 47–52, Aug. 2021. [Online]. Available: https:
//ejurnalunsam.id/index.php/jgp/article/view/3771
[18] S. Fachrurrazi, “Peramalan Penjualan Obat Menggunakan Metode Single Exponential Smoothing pada Toko Obat Bintang
Geurugok,” TECHSI - Jurnal Teknik Informatika, vol. 7, no. 1, pp. 19–30, Mar. 2019, number: 1. [Online]. Available:
https://ojs.unimal.ac.id/techsi/article/view/178
[19] R. Aminudin and Y. Handoko, “Model Peramalan Garis Kemiskinan Menggunakan Metode Double Exponential Smoothing
dari Holt,” Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi, vol. 5, no. 1, Jan. 2019. [Online]. Available:
https://ojs.unikom.ac.id/index.php/jtk3ti/article/view/2295
[20] D. Anggraeni, S. Maryani, and S. Ariadhy, “Peramalan Garis Kemiskinan di Kabupaten Purbalingga Tahun 2021-2023
dengan Metode Double Exponential Smoothing Linier Satu Parameter dari Brown,” Jurnal Ilmiah Matematika dan Pendidikan
Matematika, vol. 13, no. 2, p. 155, Dec. 2021. [Online]. Available: http://jos.unsoed.ac.id/index.php/jmp/article/view/4548
[21] R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, 2nd ed. Lexington, Ky.: Otexts, online, openaccess
textbook, 2018.
[22] I. Djakaria and S. E. Saleh, “Covid-19 forecast using Holt-Winters exponential smoothing,” Journal of Physics: Conference
Series, vol. 1882, no. 1, p. 012033, May 2021. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1882/
1/012033
[23] P. C. Bruce and A. Bruce, Practical statistics for data scientists: 50 essential concepts, first edition ed. Sebastopol, CA:
O’Reilly, 2017, oCLC: ocn936003166.
Published
2024-03-26
How to Cite
Agusdin, R., Tahalea, S., & Permadi, V. (2024). Forecasting the Poverty Rates using Holt’s Exponential Smoothing. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(2), 431-440. https://doi.org/https://doi.org/10.30812/matrik.v23i2.2672
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