Population Prediction Using Multiple Regression and Geometry Models Based on Demographic Data
Abstract
Population growth is an important issue because it significantly impacts a country’s growth and development. Large population growth can impact potential resources that drive the pace of the economy and national development. On the other hand, it can also be a problem of poverty, hunger, unemployment, education, health, and others. The government needs to control population growth to balance it with good population quality. Data sourced from the Population and Civil Registration Office of Simalungun Regency, Tanah Java sub-district has a high population and continues to increase every year. The impact of the population increase is that it affects the population’s welfare, most of whom work as laborers and farmers. To overcome this problem, it is necessary to predict the number of people in the future so that the government can make the right decisions and policies in controlling the population. This study aims to make predictions using two models, namely Multiple Linear Regression, to find linear equations and Geometry Models for population growth projections. This study utilizes multiple regression analysis and geometric models using three independent variables, namely birth rate (X1), migration rate (X2), and death rate (X3), as well as one bound variable, population number (Y). This study’s results show that the Tanah Java sub-district population is expected to increase in the next five years (2024-2028). Predictions show that by 2024, the population is expected to reach 61178 people from 59589 in 2023. Based on the results of the study, the conclusion of this study it can be used as a guide for the authorities in planning strategies and resource allocation and making a significant contribution in estimating population development in the Java region so that there will be no population explosion in the future so that it does not have a negative impact.
Downloads
References
Extreme Learning Machine ( ELM ),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 2, pp. 509–516,
2020.
[2] W. A. L. Sinaga, S. Sumarno, and I. P. Sari, “The Application of Multiple Linear Regression Method for Population Estimation
Gunung Malela District,” JOMLAI: Journal of Machine Learning and Artificial Intelligence, vol. 1, no. 1, pp. 55–64, 2022,
https://doi.org/10.55123/jomlai.v1i1.143.
[3] V. Skirbekk, “Fertility, Population Growth, and Population Composition BT - Decline and Prosper!: Changing Global Birth
Rates and the Advantages of Fewer Children,” V. Skirbekk, Ed. Cham: Springer International Publishing, 2022, pp. 329–355,
https://doi.org/10.1007/978-3-030-91611-4 18.
[4] I. Mardiyah, W. Dianita Utami, D. C. Rini Novitasari, M. Hafiyusholeh, and D. Sulistiyawati, “Analisis Prediksi Jumlah Penduduk
Di Kota Pasuruan Menggunakan Metode Arima,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 15, no. 3, pp.
525–534, 2021, https://doi.org/10.30598/barekengvol15iss3pp525-534.
[5] F. Fejriani, Hendrawansyah, and L. Muharni, “Forecasting Peningkatan Jumlah Penduduk Berdasarkan Jenis Kelamin Menggunakan
Metode Arima,” Kajian, Penelitian dan pengembangan pendidikan, vol. 8, no. 1, pp. 27–36, 2020.
[6] D. Anggreini, “Penerapan Model Populasi Kontinu Pada Perhitungan Proyeksi Penduduk Di Indonesia (Studi Kasus: Provinsi
Jawa Timur),” E-Jurnal Matematika, vol. 9, no. 4, p. 229, 2020, https://doi.org/10.24843/mtk.2020.v09.i04.p303.
[7] M. M. Maja and S. F. Ayano, “The Impact of Population Growth on Natural Resources and Farmers’ Capacity to Adapt to
Climate Change in Low-Income Countries,” Earth Systems and Environment, vol. 5, no. 2, pp. 271–283, 2021, https://doi.org/
10.1007/s41748-021-00209-6.
[8] S. Li, “Predicting the Total Population Development of China Based on Logistic Blocking Growth Model and Improved Grey
GM (1,1) Prediction Model,” in Proceedings of the 6th International Conference on Information Systems Engineering, ser.
ICISE ’21. New York: Association for Computing Machinery, 2022, pp. 26–30, https://doi.org/10.1145/3503928.3503934.
[9] A. E. Raftery and H. ˇSevˇc´ıkov´a, “Probabilistic population forecasting: Short to very long-term,” International Journal of
Forecasting, vol. 39, no. 1, pp. 73–97, 2023, https://doi.org/https://doi.org/10.1016/j.ijforecast.2021.09.001.
[10] A. Garre, J. Koomen, H. M. W. den Besten, and M. H. Zwietering, “Modeling Population Growth in R with the biogrowth
Package,” Journal of Statistical Software, vol. 107, no. 1 SE - Articles, pp. 1–51, sep 2023, https://doi.org/10.18637/jss.v107.i01.
[11] N. Alghanmi, R. Alotaibi, S. Alshammari, and A. Mahmood, “Population Fusion Transformer for Subnational Population
Forecasting,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, p. 26, 2024, https://doi.org/10.1007/
s44196-024-00413-y.
[12] 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, 2022, https://doi.org/10.30812/
matrik.v21i2.1348.
[13] M. Gregorich, S. Strohmaier, D. Dunkler, and G. Heinze, “Regression with Highly Correlated Predictors: Variable Omission
Is Not the Solution,” International Journal of Environmental Research and Public Health, vol. 18, p. 4259, apr 2021, https:
//doi.org/10.3390/ijerph18084259.
[14] L. Chen, T. Mu, X. Li, and J. Dong, “Population Prediction of Chinese Prefecture-Level Cities Based on Multiple Models,”
Sustainability (Switzerland), vol. 14, no. 8, pp. 1–23, 2022, https://doi.org/10.3390/su14084844.
[15] F. V. S¸ ahinarslan, A. T. Tekin, and F. C¸ ebi, “Application of machine learning algorithms for population forecasting,” International
Journal of Data Science, vol. 6, no. 4, pp. 257–270, jan 2021, https://doi.org/10.1504/IJDS.2021.122770.
[16] G. Adhikari, “Interpreting the Basic Results of Multiple Linear Regression,” Scholars’ Journal, pp. 22–37, dec 2022, https:
//doi.org/10.3126/scholars.v5i1.55775.
[17] A. B. Abadi, A. Fadllullah, S. Sumardi, S. Mahdi, and A. N. Juniar, “Perhitungan Indeks Massa Tubuh Less Contact Berbasis
Computer Vision dan Regresi Linear,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21,
no. 3, pp. 629–638, 2022, https://doi.org/10.30812/matrik.v21i3.1512.
[18] M. Iwasaki, “Multiple Regression Analysis from Data Science Perspective BT - Advanced Studies in Behaviormetrics and
Data Science: Essays in Honor of Akinori Okada,” T. Imaizumi, A. Nakayama, and S. Yokoyama, Eds. Singapore: Springer
Singapore, 2020, pp. 131–140, https://doi.org/10.1007/978-981-15-2700-5 8.
[19] J. Rana, P. L. Gutierrez, and J. C. Oldroyd, “Quantitative Methods BT - Global Encyclopedia of Public Administration, Public
Policy, and Governance,” A. Farazmand, Ed. Cham: Springer International Publishing, 2020, pp. 1–6, https://doi.org/10.1007/
978-3-319-31816-5 460-1.
[20] Y. Sun, X. Wang, C. Zhang, and M. Zuo, “Multiple Regression: Methodology and Applications,” Highlights in Science, Engineering
and Technology, vol. 49, pp. 542–548, may 2023, https://doi.org/10.54097/hset.v49i.8611.
[21] H. Nayebi, “Multiple Regression Analysis BT - Advanced Statistics for Testing Assumed Causal Relationships: Multiple Regression
Analysis Path Analysis Logistic Regression Analysis,” H. Nayebi, Ed. Cham: Springer International Publishing,
2020, pp. 1–46, https://doi.org/10.1007/978-3-030-54754-7 1.
[22] N. Nagajothi, S. S. Dhenakaran, and S. U. Maheswari, “A Geometric Method for Extracting Images of PDF Files,” in 2022
International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2022, pp. 1096–1100, https://doi.org/10.
1109/ICAAIC53929.2022.9792982.
Matrik:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
