Stock Price Prediction Using LSTM and XGBoost with Social Media Sentiment
-
Nisa Hanum Harani
*
International Business Logistic University, Bandung,
Indonesia
- Marismati Marismati International Business Logistic University, Bandung, Indonesia (*) Corresponding Author
Abstract
The influence of social media on financial markets is growing and motivates research on the predictive role of sentiment in stock price movements. Bank Negara Indonesia (BBNI) is part of the Danantara holding company, and BBNI's strategic position is an important indicator for measuring the performance of the broader financial ecosystem in Indonesia. This study analyzes the influence of social media sentiment on the stock price prediction of Bank Negara Indonesia (BBNI), which is part of the state-owned holding company Danantara. Historical market data is combined with sentiment indicators obtained from public conversations on X/Twitter. Daily sentiment features are then integrated with market variables, including OHLCV data, to form a combined dataset. Two machine learning approaches were employed: Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). The results revealed contrasting patterns between the two models. The LSTM Baseline consistently produced RMSE around (≈46–65) across all scenarios. However, XGBoost-Extended is the best-performing and recommended model for sentiment-integrated prediction with RMSE (≈30–40).
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