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Abdullah Abdullah
Universitas Islam Negeri Sunan Gunung Djati Bandung
Computer Science & IT

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Implementasi Algoritma Bidirectional Encoder Representations From Transformer Pada Speech To Text Untuk Notulensi Rapat Abdullah Abdullah; Jumadi; Deden Firdaus
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1725

Abstract

Meeting transcription is a crucial process for organizations, yet it often consumes significant time and resources due to the manual effort involved in recording, understanding, and documenting discussions accurately. In the digital era, advancements in speech processing and natural language understanding provide an opportunity to automate this process. This research focuses on the implementation of the Bidirectional Encoder Representations from Transformers (BERT) algorithm in a Speech-to-Text (STT) system to enhance the accuracy and efficiency of meeting transcriptions. The study integrates BERT, a deep learning-based model capable of comprehending bidirectional contextual information, into the transcription pipeline to improve handling of complex conversational contexts. The research follows a systematic methodology, starting from data preprocessing, model training, and evaluation to assess its performance. Results show that the proposed system achieves high transcription accuracy, demonstrating significant potential for real-world applications in organizational environments. This research also highlights the importance of advanced NLP technologies, such as BERT, in overcoming challenges of transcription in multilingual and noisy environments. The developed system offers practical benefits in terms of reducing manual effort and improving access to meeting documentation, making it a valuable tool for productivity enhancement.
Co-Authors Deden Firdaus Jumadi