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Abstract

The objective of this research is to find out the lexical errors made by Google Translate and Bing Translator in translating Indonesian folklore "Princess Tandampalik" and "Sigarlaki and Limbat." The research applied the qualitative method. The data were analyzed using hybrid taxonomy of error analysis from Vilar, et al. The results of this research show that Google Translate made 103 errors in total which consist of 12 missing words, 19 errors in word order, 64 incorrect words, and 8 unknown words. Meanwhile, Bing Translator made 95 errors which consist of 5 missing words, 1 error in word order, 88 incorrect words, and 1 unknown word. Incorrect word is the most frequent error found in the translation resulted from Google Translate and Bing Translator with a total of 152 errors. The incorrect words mainly occurred in the translation of adjectives and adverbs in which Google Translate and Bing Translator mostly translated them into noun form. Thus, it can be concluded that both machine translators' performances are not different because they have their advantages and disadvantages. 

Keywords

Error Analysis Translation Error Translator Machine

Article Details

How to Cite
Jufriadi, J., Asokawati, A., & Thayyib, M. (2022). The Error Analysis of Google Translate and Bing Translator in Translating Indonesian Folklore. FOSTER: Journal of English Language Teaching, 3(2), 69-79. https://doi.org/10.24256/foster-jelt.v3i2.89

References

  1. Adiputra, M. F. (2019). Error Analysis in the Performance of Google Translate and Bing Translator in Translating Children’s Story Book Pancuran Pangeran. Undergraduate Thesis, UIN Sanata Dharma Yogyakarta. Retrieved from http://repository.usd.ac.id/id/eprint/33179
  2. Almahasees, Z. M. (2018). Assessment of Google and Microsoft Bing Translation of Journalistic Texts. International Journal of Languages, Literature, and Linguistics, 4(3) pp. 231–35. https://doi.org/10.18178/ijlll.2018.4.3.178
  3. Choliludin. (2007). The Technique of Making Idiomatic Translation. Bekasi: Kesaint Blanc.
  4. Fadilah, E. M. (2017). Semantic Error Analysis of Instagram Machine Translation from Indonesian to English. Undergraduate Thesis, UIN Syarif Hidayatullah Jakarta. Retrieved from https://repository.uinjkt.ac.id/dspace/handle/123456789/35316
  5. Hu, Lilis. (2019). Seri Dongeng 3D Nusantara: Putri Tandampalik. Jakarta: Bhuana Ilmu Populer.
  6. Hu, Lilis. (2019). Seri Dongeng 3D Nusantara: Sigarlaki dan Limbat. Jakarta: Bhuana Ilmu Populer.
  7. Larson. M. L. (1998). Meaning-Based Translation: A Guide to Cross-Language Equivalence. Maryland: University Press of America.
  8. Newmark, P. (1988). A Textbook of Translation. London: Prentice-Hall International.
  9. Sayogie, F. (2014). Teori dan Praktik Penerjemahan. Tangerang Selatan: Transpustaka.
  10. Sukma, K.R.P. (2019). YouTube Auto-Generated Subtitle Performance in Translating Content in Vogue Magazine Channel. Undergraduate Thesis, UIN Sanata Dharma Yogyakarta. Retrieved from http://repository.usd.ac.id/id/eprint/36258
  11. Susanti, E. (2018). Lexical Errors Produced by Instagram Machine Translation. Undergraduate Thesis, UIN Maulana Malik Ibrahim. Retrieved from http://etheses.uin-malang.ac.id/id/eprint/14231
  12. Veronika, S. (2018). Instagram Translate’s and Human Translation’s Performance in Translating the Captions in @Basukibtp Instagram Account. Undergraduate Thesis, UIN Sanata Dharma Yogyakarta. Retrieved from http://repository.usd.ac.id/id/eprint/18344
  13. Vilar, D., J. Xu, L. F. D'Haro, and H. Ney. (2006). Error Analysis of Statistical Machine Translation Output. Proceedings of the Fifth International Conference on Language Resources and Evaluation, pp. 697–702. Retrieved from https://www.lrec-conf.org/proceedings/lrec2006/pdf/413