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All Journal Journal Software, Hardware and Information Technology MDP Student Conference Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Systems and Information Technology Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Firza Septian
Universitas Serelo Lahat
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Journal : MDP Student Conference

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Prediksi GDP dengan RF dan XGBoost Berdasarkan Aspek Sosial, Ekonomi, dan Lingkungan Mubarok, M. Husni; Septian, Firza
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11206

Abstract

This study aims to analyze Gross Domestic Product (GDP) prediction using Random Forest and XGBoost algorithms by considering social, economic, and environmental variables. The dataset was obtained from Kaggle and includes 22 independent variables influencing GDP. The model was developed with Whale Optimization Algorithm (WOA) optimization to improve prediction accuracy. Experiments were conducted on the Google Colab platform, and evaluation metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results show that XGBoost with WOA optimization achieves higher prediction accuracy compared to Random Forest. Key factors influencing GDP were identified through feature correlation analysis. In conclusion, the combination of machine learning and metaheuristic-based optimization methods enhances GDP prediction accuracy, providing valuable insights for economic policymakers.
Co-Authors Dwi Putriani, Nina Ema Utami Iski Mediansyah Kusrini , Kusrini Mubarok, M. Husni Muhammad Sulkhan Nurfatih Prakarsya, Agus Prakarsya, Agustian Putriani, Nina Dwi TONNY HIDAYAT Yusi Nurmalasari