GOLD PRICE PREDICTION SYSTEM USING THE RANDOM FOREST METHOD - Kurnia Agung Prastyo; Sibyan, Hidayatus; Nur Hasanah (Nacreva Publisher, 2025)
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.