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All Journal Jurnal Serambi Ilmu
Flora Oktaviani Chawdri
Universitas Serambi Mekkah
Education

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A Contrastive Analysis of DeepL Translation vs. Google Translate’s Performance in Rendering Academic Texts: Insights from EFL Learners Sabrina Sabrina; Mina Fadhillah; Flora Oktaviani Chawdri; Cut Safara; Lara Juniarti
Jurnal Serambi Ilmu Vol. 26 No. 1 (2025): Jurnal Serambi Ilmu
Publisher : Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jsi.v26i1.2502

Abstract

Numerous online translation tools are available today to assist academic works. This study aimed to explore English students’ insights regarding two most popular translation tools, namely DeepL Translation and Google Translate in terms of accuracy, naturalness, grammar, context understanding, terminology translation, speed, language pairs, and additional features of both tools. This research is descriptive qualitative. Thirty English students of Universitas Serambi Mekkah filled in a Likert-scale questionnaire comprising 10 items. Their responses were analyzed using Thematic Analysis. The findings showed that most participants agreed that DeepL Translation is more accurate and natural than Google Translate in rendering academic texts (58.1% and 68.8%, respectively). DeepL is also believed to have better grammar accuracy (56.3%), excels at deciphering context and language nuances (64.5%), and is faster than Google Translate (71%). Despite numerous advantages of DeepL, Google Translate is deemed superior by 68.8% participants in terms of additional features, such as voice translation, image translation, document translation, and more language pairs (61.3%), offering more advantages than DeepL. A majority of the students (67.7%) also prefer to continue using DeepL to Google Translate. This research can benefit EFL learners and academicians who require using such Neural Machine Translations for the execution of academic tasks.
Co-Authors Cut Safara Lara Juniarti Mina Fadhillah Sabrina Sabrina