Predictive Analysis of Malaysia's Population Dynamics Utilizing Exponential Growth Models and Newton-Raphson Iterative Optimization Techniques

Abstract

Changes in population and population dynamic modelling are essential for ensuring optimal resource allocation and sustain development towards guiding strategic planning. Approximately 33.9 million inhabitants can be counted as the current populations of Malaysia. The changes in populations are mainly attributed to fertility and mortality change rates, urbanization, migration, and other socio-economic factors. Most importantly, however, the annual rate of population growth decreased from one point two-five percent in 2019 to one point zero-six percent in 2024 due to the significant effect of COVID-19, which reversed or changed societal norms, increasing death rates, and created another kind of economic uncertainty. The traditional population estimation approaches, like exponential and logistic growth models, do not usually incorporate the complexity and nonlinearity associated with demographic trends. As a consequence, this research utilizes population data from the Macrotrends database for an exploration of advanced iterative numerical techniques, particularly the Newton-Raphson technique. The latter is famous for its computational efficiency, rapid convergence, and accuracy in solving nonlinear algebraic equations, and it may well serve as an attractive alternative in demographic forecasting techniques. However, its sensitivity to initial approximations and the need for derivatives are known limitations. In this study, the Newton-Raphson method is applied to Malaysian population data for the years 2019 to 2024 to predict the population count in 2025. The study thus fills a critical dogma in the application of numerical optimization techniques to demographic analysis by showing that the methodology holds promise of overcoming the shortcomings of the traditional models. Thus, it would be practically valuable and computationally efficient in modeling population growth, providing a robust base for designing simulators.