Pemanfaatan Analisis Sentimen dalam Prediksi Harga Saham: Studi pada Investor Retail Indonesia
DOI:
https://doi.org/10.25170/jm.v21i1.5184Keywords:
LSTM, Bi-LSTM, Machine Learning, Stock, SentimentAbstract
The upswing in engagement from retail investors in the Indonesian stock market aligns with a significant rise in the use of various social media platforms as conduits for stock-related information. In particular, number of content creators shared information about stock in Youtube grows, the information including the effect of corporation actions at stock market. This study sought to leverage sentiment data extracted from particular videos to predict the stock closing prices, especially at the corporate action event using Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models. In this study also included several classification algorithm result to explore the accuracy in the prediction models. The result indicate that while sentiment from Youtube serves a viable variable for prediction, the Bi-LSTM model shows better performance compared to the based model in forecasting stock prices surrounding corporate action dates. Furthermore, the combination with classification algorithms shows an improvement in refining predictions, where demonstrate a potential accuracy score when incorporated into the predictive model.
This research contributes insights the potential value using sentiment from Youtube platform and machine learning models to predict the time series data, especially in stock market. The findings hold significance for Indonesian retail investors seeking an alternative decision-making tools within the dynamic stock market landscape.
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