Application of Runge-Kutta and Adam-Bashforth-Moulton Methods for Population Growth Prediction

This research article investigates the prediction of population growth using logistic equations, focusing on the application of the fourth-order Runge-Kutta (RK4) and Adams-Bashforth-Moulton (ABM4) numerical methods. The study emphasizes the importance of accurate population growth prediction in regional development planning, particularly in the context of South Sulawesi, Indonesia. It outlines the logistic equation as a model for population dynamics, incorporating factors such as intrinsic growth rate and environmental capacity. The RK4 method is highlighted for its simplicity and accuracy in solving differential equations, while the ABM4 method is noted for its stability and low truncation error. The results indicate that the RK4 method provides predictions that align more closely with actual data compared to the ABM4 method. Additionally, the study discusses the impact of parameter variations, specifically the growth rate and population carrying capacity, on the accuracy of predictions, establishing a foundation for effective development planning.