<![CDATA[In the era of globalization, rapid technological advancements have significantly impacted the financial sector, particularly stock price movements. This study aims to contribute to financial analysis and investment by providing a predictive tool to help investors make more informed investment decisions. The Random Forest method, a machine learning al-gorithm known for effectively handling complex and heterogeneous data, is used to pre-dict stock price movements. The study utilizes historical stock data from companies listed on the Indonesia Stock Exchange (IDX) as a case study. The resulting predictive model demonstrates high accuracy, achieving 98% accuracy, with an R-squared (R²) value of 0.94 and a Mean Absolute Percentage Error (MAPE) of 0.40%. This research identifies key factors, such as Previous, High, Low, Volume, and Change, that significantly influ-ence stock price movements. The strengths of this study lie in its use of an extensive da-taset, involving 104 stock codes as examples, and its integration of interactive visualiza-tion via Streamlit to enhance data interpretation. This tool is expected to be a reliable solu-tion that provides superior predictive capabilities compared to traditional methods and supports more accurate investment analysis in the stock market.]]>
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