Perbandingan Algoritma Random Forest dan SVM untuk Prediksi Tren Harga Saham BBCA

Muhammad Hasan Rizky; Dian Hafidh Zulfikar; Hermanto Hermanto

doi:10.47065/josh.v8i1.11198

Muhammad Hasan Rizky * Universitas Islam Negeri Raden Intan Lampung, Bandar Lampung, Indonesia
Dian Hafidh Zulfikar Universitas Islam Negeri Raden Intan Lampung, Bandar Lampung, Indonesia
Hermanto Hermanto Universitas Islam Negeri Raden Intan Lampung, Bandar Lampung, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v8i1.11198

Keywords: Random Forest; Support Vector Machine; BBCA Stock Prediction; Technical Indicators; Decision Support System

Abstract

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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