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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Judea Tirta Jordan Simamora
Universitas HKBP Nommensen Pematangsiantar
Computer Science & IT

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Tinjauan Arsitektur Transformer Dan Penerapannya Pada Pemrosesan Bahasa Alami Marcel Alezandro Sihombing; Irene Lestaria Sinaga; Octav Kornelius Hutagaol; Judea Tirta Jordan Simamora; Alex Septama Sihite
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7096

Abstract

The rapid development in the field of natural language processing (NLP) cannot be separated from the emergence of the Transformer architecture first introduced in 2017. This architecture relies on a self-attention mechanism that allows the model to process all tokens at once, unlike the sequential approach in RNN or LSTM. This article presents a literature review of the Transformer architecture, its main components such as multi-head attention, positional encoding, and feed-forward network, as well as its various applications in NLP tasks such as machine translation, sentiment analysis, and text generation. In addition, the advantages and limitations of the Transformer compared to previous architectures are discussed, along with challenges such as high computational requirements and large memory consumption. This review is expected to provide a comprehensive basic understanding of the Transformer for researchers and practitioners in the field of machine learning.
Co-Authors Alex Septama Sihite Irene Lestaria Sinaga Marcel Alezandro Sihombing Octav Kornelius Hutagaol