Application of genetic algorithm and backpropagation neural networks to predict Tegal City population


  • Aang Alim Murtopo STMIK YMI Tegal, Indonesia
  • Wahyu Nursahid STMIK YMI Tegal, Indonesia
  • Nurul Fadilah STMIK YMI Tegal, Indonesia
  • Gunawan Gunawan STMIK YMI Tegal, Indonesia



Backpropagation, Genetic Algorithm, Prediction Models, Population, Tegal City


Use of Genetic Algorithms and Backpropagation Neural Networks for Population Prediction in Tegal City, which aims to create precise prediction models using advanced computational techniques. This research uses a quantitative approach that combines experimental methods, data analysis, and model validation to implement and test predictive models. By using genetic algorithms for parameter optimization and neural network backpropagation for training, the findings show that the model can accurately predict population numbers with minimal error and high determination coefficients. The implications of this study are significant for urban planning and public policy development due to the accuracy and effectiveness of the model in forecasting population growth based on historical data.


Barco, I. M. H., Amórtegui, J. C. E., & Dallos, J. A. G. (2022). Development and validation of qualitative screening, quantitative determination and post-targeted pesticide analysis in tropical fruits and vegetables by LC-HRMS. Food Chemistry, 367, 130714.

Bukhari, M. M., Alkhamees, B. F., Hussain, S., Gumaei, A., Assiri, A., & Ullah, S. S. (2021). An improved artificial neural network model for effective diabetes prediction. Complexity, 2021, 1–10.

Chen, N., Xiong, C., Du, W., Wang, C., Lin, X., & Chen, Z. (2019). An improved genetic algorithm coupling a back-propagation neural network model (IGA-BPNN) for water-level predictions. Water, 11(9), 1795.

Chung, H., & Shin, K. (2020). Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Computing and Applications, 32(12), 7897–7914.

Collins, C. J. (2021). Expanding the resource based view model of strategic human resource management. The International Journal of Human Resource Management, 32(2), 331–358.

Faybishenko, B., Versteeg, R., Pastorello, G., Dwivedi, D., Varadharajan, C., & Agarwal, D. (2022). Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data. Stochastic Environmental Research and Risk Assessment, 1–14.

Folbre, N. (2021). The rise and decline of patriarchal systems: An intersectional political economy. Verso Books.

Garud, K. S., Jayaraj, S., & Lee, M. (2021). A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models. International Journal of Energy Research, 45(1), 6–35.

Gietel-Basten, S., & Sobotka, T. (2021). Trends in population health and demography. The Lancet, 398(10300), 580–581.

Jain, A., Gue, I. H., & Jain, P. (2023). Research trends, themes, and insights on artificial neural networks for smart cities towards SDG-11. Journal of Cleaner Production, 412, 137300.

Kanwal, S., Rasheed, M. I., Pitafi, A. H., Pitafi, A., & Ren, M. (2020). Road and transport infrastructure development and community support for tourism: The role of perceived benefits, and community satisfaction. Tourism Management, 77, 104014.

Kovacs, D. J., Li, Z., Baetz, B. W., Hong, Y., Donnaz, S., Zhao, X., Zhou, P., Ding, H., & Dong, Q. (2022). Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study. Journal of Membrane Science, 660, 120817.

Kutty, A. A., Wakjira, T. G., Kucukvar, M., Abdella, G. M., & Onat, N. C. (2022). Urban resilience and livability performance of European smart cities: A novel machine learning approach. Journal of Cleaner Production, 378, 134203.

Langazane, S. N., & Saha, A. K. (2022). Effects of particle swarm optimization and genetic algorithm control parameters on overcurrent relay selectivity and speed. IEEE Access, 10, 4550–4567.

Li, G., Zhang, A., Zhang, Q., Wu, D., & Zhan, C. (2022). Pearson correlation coefficient-based performance enhancement of broad learning system for stock price prediction. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(5), 2413–2417.

Li, L. (2022). Reskilling and upskilling the future-ready workforce for industry 4.0 and beyond. Information Systems Frontiers, 1–16.

Li, W., & Yi, P. (2020). Assessment of city sustainability—Coupling coordinated development among economy, society and environment. Journal of Cleaner Production, 256, 120453.

Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99.

Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Fundamentals of artificial neural networks and deep learning. In Multivariate statistical machine learning methods for genomic prediction (pp. 379–425). Springer.

O’Sullivan, O., Holdsworth, D. A., Ladlow, P., Barker-Davies, R. M., Chamley, R., Houston, A., May, S., Dewson, D., Mills, D., & Pierce, K. (2023). Cardiopulmonary, functional, cognitive and mental health outcomes post-COVID-19, across the range of severity of acute illness, in a physically active, working-age population. Sports Medicine-Open, 9(1), 7.

Qi, J., Du, J., Siniscalchi, S. M., Ma, X., & Lee, C.-H. (2020). Analyzing upper bounds on mean absolute errors for deep neural network-based vector-to-vector regression. IEEE Transactions on Signal Processing, 68, 3411–3422.

Raghunath, S., Ulloa Cerna, A. E., Jing, L., VanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., & Hafez, A. (2020). Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature Medicine, 26(6), 886–891.

Salam, A. A. (2023). Ageing in Saudi Arabia: new dimensions and intervention strategies. Scientific Reports, 13(1), 4035.

Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization. Archives of Computational Methods in Engineering, 28(4), 2133–2164.

Wilson, T., Grossman, I., Alexander, M., Rees, P., & Temple, J. (2022). Methods for small area population forecasts: State-of-the-art and research needs. Population Research and Policy Review, 41(3), 865–898.

Ye, Y., Wang, Z., & Zhang, X. (2020). An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error. Structural and Multidisciplinary Optimization, 62, 529–542.

Zhou, Y., Wang, Y., Wang, K., Kang, L., Peng, F., Wang, L., & Pang, J. (2020). Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors. Applied Energy, 260, 114169.




How to Cite

Murtopo, A. A., Nursahid, W., Fadilah, N., & Gunawan, G. (2024). Application of genetic algorithm and backpropagation neural networks to predict Tegal City population. Jurnal Mandiri IT, 13(1), 91–98.

Most read articles by the same author(s)

1 2 > >>