Utilizing X Sentiment Analysis to Improve Stock Price Prediction Using Bidirectional Long Short-Term Memory
Dublin Core
Title
Utilizing X Sentiment Analysis to Improve Stock Price Prediction Using Bidirectional Long Short-Term Memory
Subject
BERT, BiLSTM, sentiment analysis, stock price prediction, X
Description
The capital market is one of the important factors that influence the national economy. However, the
stock price in capital market fluctuates over time. Therefore, the investors strongly need an accurate
prediction of stock price for making profitable decision. However, with the pervasive influence of the
internet, investors and investment institutions have started incorporating online opinions and news,
including those found on social media platforms like X. This research aims to enhance stock price
prediction by utilizing X sentiment analysis. The sentiment of tweets from X related to IHSG stock
price is predicted by using BERT (Bidirectional Encoder Representations from Transformers), then its result isintegrated with the historical stock price data for predicting future stock price by using BiLSTM (Bidirectional Long Short-Term Memory). The experiment results show that the RMSE and MAPE of the proposed model with sentiment analysis is decreased by 0.042 and 0.595, resepectively, compared to the model without sentiment analysis. Therefore, it can be concluded that the inclusion of X sentiment analysis in conjunction with BiLSTM succeeded in improving the performance of stock price prediction. The study's outcome is expected to be valuable for investors to make profitable decisions, leveraging the information available on social media.
stock price in capital market fluctuates over time. Therefore, the investors strongly need an accurate
prediction of stock price for making profitable decision. However, with the pervasive influence of the
internet, investors and investment institutions have started incorporating online opinions and news,
including those found on social media platforms like X. This research aims to enhance stock price
prediction by utilizing X sentiment analysis. The sentiment of tweets from X related to IHSG stock
price is predicted by using BERT (Bidirectional Encoder Representations from Transformers), then its result isintegrated with the historical stock price data for predicting future stock price by using BiLSTM (Bidirectional Long Short-Term Memory). The experiment results show that the RMSE and MAPE of the proposed model with sentiment analysis is decreased by 0.042 and 0.595, resepectively, compared to the model without sentiment analysis. Therefore, it can be concluded that the inclusion of X sentiment analysis in conjunction with BiLSTM succeeded in improving the performance of stock price prediction. The study's outcome is expected to be valuable for investors to make profitable decisions, leveraging the information available on social media.
Creator
Marcella Komunita Pasaribu, Fahrel Gibran Alghany, Benhard Simanullang, Hilma Nur Khasanah, Hanan
Nurul Hardyana Zain, Khadijah, and Rismiyati
Nurul Hardyana Zain, Khadijah, and Rismiyati
Source
http://dx.doi.org/10.21609/jiki.v18i1.1428
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2025-02-08
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Marcella Komunita Pasaribu, Fahrel Gibran Alghany, Benhard Simanullang, Hilma Nur Khasanah, Hanan
Nurul Hardyana Zain, Khadijah, and Rismiyati, “Utilizing X Sentiment Analysis to Improve Stock Price Prediction Using Bidirectional Long Short-Term Memory,” Repository Horizon University Indonesia, accessed May 23, 2025, https://repository.horizon.ac.id/items/show/8943.