Education Quarterly Reviews

ISSN 2621-5799

Published: 14 December 2020

The Accuracy and Shortcomings of Google Translate Translating English Sentences to Indonesian

Adi Sutrisno

Universitas Gadjah Mada, Indonesia

pdf download

Download Full-Text Pdf


Pages: 555-568

Keywords: Google Translate, Memsource, Statistical Machine Translation, English-Indonesian Translation, Sentence-Pair Matrix


Google Translate is a free and practical online translation service that allows millions of people around the globe to translate words, phrases, sentences, and paragraphs into an intended target language. However, in 2015, some Google Translate users in Indonesia, filed complaints, asserting that the machine was often inaccurate, speculating that it could only translate languages at the micro-level of words and phrases, rather than complete sentences or paragraphs. This research works to examine the accuracy as well as the shortcomings of Google Translate, in the context of English to Indonesian translations, in order to critically engage the complaints made by Google users. For the purpose of this study, 80 English sentences were translated using Google Translate and assessed for accuracy using a table adapted from Memsource criteria. Both the original sentences and their translated versions were analyzed using a sentence pair matrix to determine the machine’s failings and areas for improvement. The results challenged those initial speculations which suggested Google Translate is only effective with words and phrases. On the contrary, Memsource proved to be a useful tool in demonstrating a reasonable level of accuracy, accurately translating 60.37% of Indonesian-English sentences and vice versa.


  1. Aitken and Balan. (2011). ‘An Analysis of Google Translate Accuracy’. Translation Journal. Retrieved from

  2. Barreiro, et al. (2014). Linguistic Evaluation of Support Verb Construction by OpenLogos and Google Translate.

  3. Retrieved from

  4. Butler. (2010). Machine Versus Human: Will Google Translate Replace Professional Translators?. Retrieved from Research% 20 Paper %20 Butler.pdf

  5. Farlin, S. (2015). Semantics analysis in the translation of Indonesian abstract into English using Google Translate. Retrieved from

  6. Graesser, Li, and Chai. (2014). ‘Comparison of Google Translate with Human Translation’. Proceedings of the Twenty Seventh International Florida Artificial Intelligence Research Society Conference. Retrieved from


  8. Grajales. (2015). Statistics Behind Goodle Translate. Retrieved from


  10. Hardin & Picot. (1990) ‘Unchangement de point de vue qui permet d’exprimer de manière différente une même

  11. phénomène’. Translate: Initiation à la pratique de la traduction, Bordas, Paris: Aubin Imprimeur, p. 21

  12. Koehn, P. Och, F.J. and Marcu, D. (2003) ‘Statistical Phrase Based Translation’. In Proceedings of

  13. the 2003 conference of the North American Chapter of the Association for Computational Linguistics on

  14. Human Technology, Vol 1, 48-54. Edmonton, Canada: Association for computational Linguistics.

  15. Liputan 6. (2018). Alasan Orang Indonesia Doyan Pakai Google Translate. Retrieved from


  17. Memsource (2016). Data: Machine and Professional Human translations Identical in 5-20% cases. Retrieved from

  18. Napitupulu. (2017). “Analyzing Indonesian-English Abstracts Translation in Views of Translation Errors by Google Translate”. International Journal of English Language and Linguistics Research Vol.5, No.2, pp.15-23

  19. Ney, H. (1995). ‘On the Probabilistic Interpretation of Neural Network Classifiers and Discriminative Training

  20. Criteria’. IEEE Transactions on Pattern analysis and Machine Intelligence 17 (2); 107-119

  21. Okpor. (2015). Machine Translation Approaches: Issues and Challenges. IJCSI International Journal of Computer Science Issues. Vol 11. Issue 5. No 2. September 2014

  22. Osborne, et al. (2006). ‘Improved statistical machine translation using paraphrases’, Proceedings of the main

  23. conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, p.17-24, June 04-09

  24. Osborne. (2010). Metrics for MT Evaluation: Evaluating Reordering”. DOI: 10.1007/s10590-009-9066-5

  25. Papineni, et al. (2002). ‘BLEU: A Method for Automatic Evaluation of Machine Translation’. In Proceedings of the 40th Annual Meeting on association for Computational Linguistics, 311-318, Philadelphia.

  26. Patil and Davies. (2014). Use of Translate in Medical Communication: Evaluation of Accuracy. Retrieved from


  28. Sneddon, J. (1996). Indonesian: A Comprehensive Grammar. London : Routledge.

About Us

The Asian Institute of Research is an online and open-access platform to publish recent research and articles of scholars worldwide. Founded in 2018 and based in Indonesia, the Institute serves as a platform for academics, educators, scholars, and students from Asia and around the world, to connect with one another. The Institute disseminates research that is proven or predicted to be of significant influence for the general public.

Contact Us

Please send all inquiries to the email:

Business Address:

5th Floor, Kavling 507, Fajar Graha Pena Tower, Jl. Urip Sumohardjo No.20, Makassar, Indonesia 90234

Copyright © 2018 The Asian Institute of Research. All rights reserved

Stay Connected

  • Instagram - Black Circle
  • Facebook - Black Circle
  • LinkedIn - Black Circle