<![CDATA[A lack of financial literacy and a fear of investment risk often lead university students to engage in stock speculation without objective decision-making tools. This study aims to develop a machine learning-based predictive model to classify daily stock price movement trends for PT Bank Central Asia Tbk (BBCA) during the 2020-2026 period, a popular investment choice among students. A comparative experimental method is employed to evaluate and compare the performance of the Random Forest (RF) algorithm against the Support Vector Machine (SVM) algorithm using a Radial Basis Function (RBF) kernel. SMA-10, RSI-14, MACD, and Signal Line technical indicators were used to extract 1,151 rows of historical data from 2020 to 2026. The data was then divided chronologically using an 80:20 ratio. According to experimental data, the SVM model outperformed Random Forest (accuracy: 52.41%; precision: 43.15%) in terms of global accuracy (57.88%) and precision (47.69%). Nevertheless, the SVM model turned out to be unduly cautious, producing a poor recall of 24.22% and, as a result, losing many chances to spot rising (bullish) patterns. Random Forest, on the other hand, showed better balanced generalization ability, with an F1-score of 45.99% and a recall of 49.22%. This study introduces the integration of momentum indicators to mitigate FOMO among novice investors. As a result, Random Forest is suggested as the decision engine architecture for creating a system that helps students make investment decisions.]]>
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