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All Journal Bulletin of Electrical Engineering and Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama
Irawan Irawan
Universitas Dinamika Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering

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HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS Jasmir; Maria Rosario; Irawan Irawan; Agus Siswanto; Tiko Nur Annisa
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7488

Abstract

The development of internet technology and social media has driven the increasing use of sentiment analysis to understand public opinion. This study aims to improve the classification performance of sentiment analysis by proposing a hybrid model that combines FastText-BLSTM and BERT. The dataset used consists of 900 Indonesian-language Netflix app user reviews obtained through crawling using Google Play Scraper. The research stages include text preprocessing, feature extraction using FastText and BERT, and classification using BLSTM, which are then combined in a concatenation layer to produce a richer feature representation. Experimental results show that the FastText-BLSTM-BERT hybrid model provides the best performance with an accuracy of 94.22%, a precision of 95.98%, a recall of 95.68%, and an F1-score of 95.83%. This achievement is superior to the single models of FastText-BLSTM and BERT. The main novelty of this research lies in the integration of contextual embeddings from BERT with subword-level semantic and sequential representations from FastText-BLSTM, which has not been extensively explored in prior studies on Indonesian sentiment analysis. This hybridization demonstrates significant improvement in model generalization and robustness for low-resource language texts
Co-Authors Afrizal Nehemia Toscany AGUS AGUS SETIAWAN Agus Siswanto Akwan Sunoto Chindra Saputra Desi Kisbianty Desi Kisbianty, Desi Errissya Rasywir FA Bambang Sukoco Gilang Prakoso, Amandha IBNU SANI Istoningtyas , Marrylinteri Istoningtyas, Marrylinteri Jasmir KARMAN, ZULFI Kisbianty , Desi Lola Yorita Astri, Lola Yorita M Irwan Bustami Maria Rosario Marrylinteri Nugroho, M. Restu ROBY SETIAWAN SIKA, XAVERIUS Tiko Nur Annisa Willy Yovi Pratama yulia