Analysis And Prediction Of Global Population Using Random Forest Regression - Banjarnahor, Jepri; JetaJones, Catherine; Gulo, Esthin Mitra; Sianturi, Angelia Chrismeshi Sheila (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2024)
This research evaluates the performance of the random forest regression algorithm in predicting global population growth from time series data. The findings indicate that population growth predictions remain stable, with an annual increase of less than 1%. Model analysis using evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and model scores demonstrates high quality, with average values below 0.5. These results imply that the model can deliver optimal and consistent outcomes. The model shows potential for accurate predictions when tested on datasets. Further analysis reveals a population increase of 0.88% in 2024, equating to an addition of approximately 70,206,291 people, and a rise of 0.91% in 2025, adding about 73,524,552 people