The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation
DOI:
https://doi.org/10.24256/ideas.v13i2.7444Keywords:
Accuracy, Clarity, Comic, Translation, Machine TranslationAbstract
This study examined the accuracy and clarity of machine translation in the context of fictional texts, with a specific focus on the comic Academy’s Genius Swordmaster. Using Google Translate as the primary tool, a qualitative analysis was conducted to evaluate how effectively the machine translated the comic’s content. The results revealed that 63% of the translated text contained errors, with many issues related to accuracy such as overly literal translations, incorrect word choices, and loss of intended meaning. Problems with clarity were also frequent, as many sentences sounded unnatural, too formal, or awkward in Indonesian, disrupting the flow and making them harder to read. These shortcomings were particularly noticeable in emotional storytelling, where tone, character expression, and cultural nuance play a key role in delivering the intended message. The findings highlight that despite recent advancements, current MT systems still face significant challenges in rendering creative and context-sensitive texts. Future research could expand this work by testing multiple MT systems or exploring post-editing approaches to improve translation quality in fictional narratives.
References
Chesterman, A. (2017). Reflective Translation Studies: Translation as an Ethical and Social Practice. Routledge. https://doi.org/10.4324/9781315645357
Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications.
Guerberof-Arenas, A., & Toral, A. (2022). Creativity in translation: Machine translation as a constraint for literary texts. Target: International Journal of Translation Studies, 34(1), 27–51. https://doi.org/10.1075/target.21025.gue
Ismayanti, D., Said, Y. R., Usman, N., & Nur, M. I. (2024). The Students Ability in Translating Newspaper Headlines into English A Case Study. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 12(1), 108-131.
Jiménez-Crespo, M. A. (2017). Translation and Web Localization. Routledge. https://doi.org/10.4324/9781315747326
Kirchhoff, K., Turner, A., & Axelrod, A. (2016). Toward multilingual neural machine translation with universal encoder and decoder. In Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (AMTA).
Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. Proceedings of the First Workshop on Neural Machine Translation, 28–39. https://doi.org/10.18653/v1/W17-3204
Masruddin, M., & Nasriandi, N. (2022). Lexical and Syntactical Errors Performed by Junior High School Student in Writing Descriptive Text. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 10(1), 1094-1100.
McCloud, S. (1993). Understanding Comics: The Invisible Art. HarperCollins.
Munday, J. (2016). Introducing Translation Studies: Theories and Applications (4th ed.). Routledge. https://doi.org/10.4324/9781315692886
Newmark, P. (1988). A Textbook of Translation. Prentice Hall.
Nida, E. A., & Taber, C. R. (1969). The Theory and Practice of Translation. Brill.
O’Hagan, M. (2016). Massively open translation: Unpacking the relationship between technology and translation in the 21st century. International Journal of Communication, 10, 929–946. https://ijoc.org/index.php/ijoc/article/view/3496
Saldanha, G., & O’Brien, S. (2013). Research Methodologies in Translation Studies. Routledge. https://doi.org/10.4324/9781315760097
Toral, A., & Way, A. (2018). What level of quality can neural machine translation attain on literary text? In Translation Quality Assessment: From Principles to Practice, 263–287. https://doi.org/10.1075/btl.141.15tor
Wan, Y., Yang, B., Wong, D. F., Chao, L. S., Yao, L., Zhang, H., & Chen, B. (2022). Challenges of neural machine translation for short texts. Computational Linguistics, 48(2), 321–342. https://doi.org/10.1162/coli_a_00435
Zhang, J., & Zong, C. (2020). Neural machine translation: Challenges, progress and future. Science China Technological Sciences, 63(10), 2028–2050. https://doi.org/10.1007/s11431-020-1632-x
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2025 Arya Nurusy Syifa, Ruminda, Ice Sariyati
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See the Effect of Open Access)
