<![CDATA[Blue chip stock price movements within the LQ45 index are non-linear and stochastic, causing conventional statistical approaches to fail in generating reliable predictions. This study develops and evaluates a hybrid deep learning architecture combining IndoBERT—a BERT-based language model pre-trained on Indonesian-language corpora—with Long Short-Term Memory (LSTM) networks to classify daily stock price trend direction (up/down). Trend labels are derived from daily return values: a return greater than zero is labeled Up (1), otherwise Down (0). Sentiment scores were extracted via IndoBERT fine-tuning from 9,819 Indonesian-language financial news articles (CNBC Indonesia) and merged with historical OHLCV data and technical indicators as model features. Experiments were conducted on five LQ45 blue chip equities: BBRI.JK, BBNI.JK, BBCA.JK, BMRI.JK (Financial Sector), and GOTO.JK (Technology). The hybrid model outperformed the baseline on three of five equities, with the highest accuracy improvement on BBCA.JK (+23.21 points, from 33.93% to 57.14%) and BMRI.JK (+12.50 points, from 46.43% to 58.93%). The overall average Relative Error Reduction (RER) reached +9.81%, demonstrating that integrating IndoBERT sentiment significantly enhances LSTM-based stock trend prediction in the Indonesian capital market.]]>
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