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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Martabe : Jurnal Pengabdian Kepada Masyarakat Aulad : Journal on Early Childhood Jurnal Teknik Informatika (JUTIF) Annual Conference on Islamic Early Childhood Education Jurnal Pengabdian Masyarakat Aufa (JPMA) Jurnal Informatika Ekonomi Bisnis JOSAR (Journal of Students Academic Research) Jurnal Aktual Pendidikan Indonesia Journal of Advanced Studies in Management
ulfa, mutia
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Neuroscience Public Health Social Sciences Other

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Sentiment Analysis Using Bidirectional Encoder Representations from Transformers for Indonesian Stock Price Prediction with Long Short-Term Memory and Gated Recurrent Unit Models Iswavigra, Dwi Utari; Setiawan, Very Dwi; Ulfa, Mutia; Ommr, Brieva
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5383

Abstract

The advancement of artificial intelligence based market analytics has driven the need for stock price prediction models capable of representing market behavior both technically and psychologically. This study aims to improve stock price forecasting in the Indonesian capital market by integrating sentiment analysis with deep learning time-series models. It evaluates whether public sentiment can contribute to enhancing prediction accuracy when combined with historical stock data. Textual sentiments were extracted using IndoBERT and converted into positive, negative, and neutral scores, which were then merged with historical stock prices. These data were modeled using LSTM, GRU, and a hybrid LSTM–GRU architecture. Model evaluation was conducted using MSE, MAE, RMSE, and MAPE metrics across six Indonesian stocks ANTM, BBCA, BBRI, SCMA, TLKM, and UNVR. The hybrid LSTM–GRU model produced the lowest prediction errors for BBCA and BBRI, with MSE scores of 0.151 and 1022.062, respectively. GRU delivered the best performance for highly volatile stocks, such as SCMA MAPE 1.65% and UNVR MAPE 0.51%, while LSTM demonstrated the most stable performance for TLKM with an MSE of 606.93 and RMSE of 24.63. Across all cases, sentiment scores improved model responsiveness, particularly during price spikes ANTM mid-2025 and price declines BBRI early year. The integration of sentiment significantly enhances prediction relevance by combining psychological market indicators with technical price trends. This framework provides more reliable decision-making support for investors, strengthens algorithmic trading strategies in Indonesia, and contributes to intelligent financial analytics that reflect local market behavior.
Co-Authors Anggiratih, Endang Aulia, Tazkiyatul Bagas Dwi Yulianto Budiono, Graceilla Kristia Seraphim Depari, Desli Lonika Br Doddy Setiawan Eko Sri Wahyuni Harisman Saputra, Rocky Iswavigra, Dwi Utari Lailiyah, Nita Ilmiyatul Mar'atullatifah, Yulaikha Mardiani, Novita Margfirah Mursalim Mursalim Na'imah, Na'imah Na’imah, Na’imah Ommr, Brieva Prasetyo, Deny Ruqiah Ganda Putri Panjaitan Safrizal Serenade, Vincensia setiawan, very dwi Suhariati, Nadia Suyahman Suyahman TAMTOMO, AGATHA PRICILLIA SEKAR Utari Iswavigra, Dwi Wicaksono, Ardy