Analisis dan prediksi Harga Saham menggunakan Metode Random Forest

Abstract

Stock price movements are highly volatile and influenced by various factors, making the investment decision-making process challenging, particularly for investors who rely on historical data. This study aims to analyze the performance of the Random Forest method in predicting the stock price of PT Bank Central Asia Tbk (BBCA) based on technical indicators. The methodology involves processing historical stock price data using a time-series approach, calculating technical indicators such as Exponential Moving Average, Moving Average, Relative Strength Index, Moving Average Convergence Divergence, and trading volume, as well as training a Random Forest model. The dataset is chronologically divided into training and testing sets to maintain temporal consistency. Model evaluation is conducted using Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and the coefficient of determination. The results indicate that the Random Forest model provides strong predictive performance, achieving a Mean Absolute Error of 202.91, a Root Mean Squared Error of 260.83, a Mean Absolute Percentage Error of 2.13%, and a coefficient of determination of 0.85. Feature contribution analysis reveals that the 12-period Exponential Moving Average has the most dominant influence on the prediction results. Based on these findings, it can be concluded that the Random Forest method is effective for predicting stock prices based on technical indicators and has the potential to support investment decision-making.