GOLD PRICE PREDICTION SYSTEM USING THE RANDOM FOREST METHOD

Authors

  • Kurnia Agung Prastyo Universitas Sains Al-Qur’an
  • Hidayatus Sibyan Universitas Sains Al-Qur'an
  • Nur Hasanah Universitas Sains Al-Qur’an

Keywords:

gold price prediction, Random Forest, machine learning, investment

Abstract

Gold is one of the most important commodities, serving as an investment instrument and a hedge against inflation. The high volatility of gold prices demands accurate predictions to support investment decision-making. This study aims to develop a gold price prediction system using the Random Forest method based on machine learning. The dataset used consists of daily gold prices from Yahoo Finance covering the period from 2020 to 2024. The research stages include data collection, preprocessing, model training, evaluation, and implementation into an interactive website. Evaluation results show a MAE of 329.31, MSE of 148,599.40, RMSE of 385.49, and a negative R² value (-1.97), indicating the model is not yet accurate. However, the system can provide a general overview of gold price trends and can be further improved to enhance prediction accuracy.

References

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Published

2025-10-31

Issue

Vol. 4 No. 1 (2025): October

Section

Articles

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Copyright (c) 2025 Clean Energy and Smart Technology

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