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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal TIMES Scientific Journal of Informatics Jurnal Informatika IT JOURNAL RESEARCH AND DEVELOPMENT Building of Informatics, Technology and Science International Journal of Engineering, Science and Information Technology TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal INFOTEL
Kelvin Leonardi Kohsasih
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Penerapan Algoritma Transformer dalam Aplikasi Parafrase Teks Otomatis Robet Robet; Kelvin Leonardi Kohsasih; Jenime Darwin
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp103-109

Abstract

The development of Natural Language Processing (NLP) technology has enabled the creation of automated text manipulation applications, one of which is text paraphrasing. This study aims to implement a Transformer architecture with a focus on Indonesian text for automatic text paraphrasing applications. The model used is a pre-trained Text-to-Text Transfer Transformer (T5), which is fine-tuned using an Indonesian text corpus called the Indo-T5 model. During the training process, the model is trained to understand language structure and context in order to generate paraphrases that are not only grammatically correct but also semantically preserved. Evaluation was conducted using BLEU and ROUGE metrics to measure the similarity between the generated paraphrased texts and manual references. The evaluation results show that the model is capable of producing coherent, relevant paraphrased texts with a good level of lexical variation with a BLEU score of 50.1, and ROUGE-L of 61.7. Thus, this study demonstrates that Transformer-based models can be effectively applied to the task of text paraphrasing in Indonesian.
Co-Authors Agung Rizky, Muhammad Dipo Alvin Alvin, Alvin Andy Andy B. Herawan Hayadi Daniel Smith Sunario Fahriyani, Tasya Ginting, Manan Hartono Hartono Hutagalung, Manahan Jenime Darwin Joni Joni Kurnia, Dian Laurendio, Fedro M. Yogi Riyantama Isjoni Muhammad Dipo Agung Rizky Muhammad Khahfi Zuhanda Octara Pribadi Pieter Octaviandy, Pieter Rika Rosnelly Rika Rosnelly, Rika Robet Robet Tarigan, Nurliana Veronica Wijaya, Veronica Willy Wira Widjaja Zakarias Situmorang