<![CDATA[The bacterial population growth model is one way to understand the dynamics of microorganisms under various environmental conditions. This study aims to model the growth of bacterial populations using the Runge-Kutta method as a numerical approach to solve differential equations describing growth rates. This method was chosen because of its high accuracy in predicting the solution value at a given time interval compared to other numerical methods. In this study, a logistics model was applied that considered factors such as environmental capacity and the intrinsic growth rate of bacteria. The initial population data and model parameters were processed using the fourth-order Runge-Kutta method, which was then validated with analytical solutions or simulations based on experimental data. The results of the analysis show that this method is able to predict bacterial growth patterns with minimal error rates. In addition, this method is also flexible to be applied to scenarios with variable parameters, such as environmental changes or the influence of antibiotics. The conclusions of this study show that the Runge-Kutta method is an effective tool for modeling the dynamics of bacterial growth, providing a more accurate picture of population changes over time. These findings have the potential to support the development of strategies in various fields, such as biotechnology, waste treatment, and microorganism infection control. Further research is recommended to integrate other external factors to improve the accuracy of the model.]]>
