Analysis And Prediction Of Global Population Using Random Forest Regression
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5312Abstract
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
References
A. Lasarudin dan R. Maku, “PREDIKSI PERTUMBUHAN JUMLAH PENDUDUK MENGGUNAKAN ALGORITMA NEURAL NETWORK (NN),” 2021.
P. Kurniawan dkk., “Prediksi Jumlah Penduduk Jakarta Selatan Menggunakan Metode Regresi Linear Berganda,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 10, no. 4, hlm. 518, Des 2022.
R. Kurnia Armanda, “Prediksi Pertumbuhan Penduduk Kecamatan Cimaragas Kabupaten Ciamis Dengan Metode Artificial Neural Network,” vol. 3, no. 2, hlm. 170–178, 2023.
A. Pratama Yudha, R. Puji Cahyono, S. Informasi Akuntansi, dan T. Komputer, “Analisis Kepuasan Pengunjung Menggunakan Metode Random Forest Untuk Wisata Pantai pada Pesawaran.”
N. Nur, F. Wajidi, S. Sulfayanti, dan W. Wildayani, “Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga,” Jurnal Komputer Terapan, vol. 9, no. 1, hlm. 58–64, Jun 2023.
L. Britanthia, C. Tanujaya, B. Susanto, dan A. Saragih, “Perbandingan Metode Regresi Logistik dan Random Forest untuk Klasifikasi Fitur Mode Audio Spotify,” Indonesian Journal of Data and Science (IJODAS), vol. 1, no. 3, hlm. 68–78, 2020.
F. Al Farikhi dkk., “Perbandingan Algoritma Classification and Regression Tree (CART) dan Random Forest (RF) untuk Klasifikasi Penggunaan Lahan pada Google Earth Engine Informasi artikel A B S T R A K Sejarah artikel.”
J Banjarnahor, F Sinaga, DS Sitorus, WAA Sitanggang, Application of Decision Tree Method in ECG Signal Classification For Heart Disorder Detection- Sinkron: jurnal dan penelitian teknik informatika, 2024.
W Setiawan, J Banjarnahor, MF Shandika, M Radhi, ANALYSIS OF CLASSIFICATION OF LUNG CANCER USING THE DECISION TREE CLASSIFIER METHOD, - Jurnal Sistem Informasi dan Ilmu Komputer Prima …, 2023
J Banjarnahor, F Zai, J Sirait, DW Nainggolan, Comparison Analysis of C4. 5 Algorithm and KNN Algorithm for Predicting Data of Non-Active Students at Prima Indonesia University- Sinkron: jurnal dan penelitian teknik informatika, 2023
SH Sinaga, AAM Duha, J Banjarnahor, Analisis Prediksi Deteksi Stroke Dengan Pendekatan Eda Dan Perbandingan Algoritma Machine Learning - Jurnal Ilmiah Betrik, 2023
A Tanzil, RA Barasa, Y Laia, J Banjarnahor, Analysis of Method C5. 0 in Triggering Factors The Number of Covid-19 Increases or Decreases After Getting the Vaccine, - Jurnal Sistem Informasi dan Ilmu Komputer Prima …, 2023
K. Ciptady, M. Harahap, J. Jonvin, Y. Ndruru, dan I. Ibadurrahman, “Prediksi Kualitas Kopi Dengan Algoritma Random Forest Melalui Pendekatan Data Science,” Data Sciences Indonesia (DSI), vol. 2, no. 1, Sep 2022.
“ANALISIS PERBANDINGAN ALGORITMA C4.5 DAN ALGORITMA KNN UNTUK MEMPREDIKSI DATA MAHASISWA NON AKTIF DI UNIVERSITAS PRIMA INDONESIA.”
A. Rizal, D. C. R. Novitasari, dan Moh. Hafiyusholeh, “Pengelompokan Karyawan Berdasarkan Kesalehan Menggunakan Perbandingan Fuzzy C-Means, K-Means, dan Probabilistic Distance Clustering,” Jurnal Fourier, vol. 11, no. 2, hlm. 69–77, Okt 2022.
P. Rosyani, A. Suhendi, D. H. Apriyanti, dan A. A. Waskita, “Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 3, hlm. 253–259, Des 2021.
L. Wulandari dan B. Olga Yogantara, “Algorithm Analysis of K-Means and Fuzzy C-Means for Clustering Countries Based on Economy and Health,” vol. 15, no. 2, hlm. 1979–276, 2022, doi: 10.30998/faktorexacta.v15i2.12106.
Fadellia Azzahra, N. Suarna, dan Y. Arie Wijaya, “Penerapan Algoritma Random Forest Dan Cross Validation Untuk Prediksi Data Stunting,” Kopertip : Jurnal Ilmiah Manajemen Informatika dan Komputer, vol. 8, no. 1, hlm. 1–6, Feb 2024.
A. Fauzan dan D. Ahmad, “ANALISIS HASIL PREDIKSI MAGNITUDO GEMPA DI WILAYAH KOTA PADANG MENGGUNAKAN TEKNIK RANDOM FOREST,” Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 4, no. 3, hlm. 1569–1576, Des 2023.
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