Performance Evaluation of Quadratic Polynomial Regression Model for Population Growth Prediction

Nur Istiqamah, Syaharuddin Syaharuddin, Vera Mandailina

Abstract


Abstract: This study aims to assess the effectiveness of the quadratic polynomial regression model in forecasting population growth in Dompu Regency. The approach used is quantitative-experimental with a focus on numerical analysis and application of mathematical models. Secondary data in the form of annual population numbers from 2016 to 2025 were obtained from the Central Statistics Agency (BPS) of Dompu Regency. Modeling was conducted using MATLAB software, while model accuracy was evaluated through two metrics: Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The forecasting results showed irrational predicted values in 2026 and 2027, including negative population estimates. In addition, the MSE value of 3,456,657,665.76 and MAPE of 8281.27% indicate a very high level of prediction error. These findings indicate that the quadratic polynomial regression model is not suitable for describing the dynamics of population growth in the region. Therefore, it is necessary to explore alternative models, such as time series methods or machine learning-based approaches, in order to obtain more accurate prediction results and support population policy formulation more effectively.

Keywords


Quadratic Polynomial Regression, Demographic Prediction, Mse, Mape, Dompu District.

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