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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer
JetaJones, Catherine
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Decision Sciences, Operations Research & Management

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Analysis And Prediction Of Global Population Using Random Forest Regression Banjarnahor, Jepri; JetaJones, Catherine; Gulo, Esthin Mitra; Sianturi, Angelia Chrismeshi Sheila
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5312

Abstract

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
Co-Authors Banjarnahor, Jepri Gulo, Esthin Mitra Sianturi, Angelia Chrismeshi Sheila