The Impact of Machine’s and Students’ Translation on Accuracy of Roda Kehidupan

Authors

DOI:

https://doi.org/10.21512/lc.v17i2.9971

Keywords:

machine translation, student translation, translation accuracy

Abstract

The research aimed to describe the impact of machine translation on translation accuracy. Machine translation was widely used to translate the original language to the target. Accuracy was a crucial thing that the translator needed to restructure in the target language. The research applied a qualitative method with sampling based on its criteria. In addition, the research had two types of data: objective and effective. There were two instruments used to collect data; the first was instruction for translating a short film entitled “Roda Kehidupan”. The students were asked to translate a short film with the help of a machine, without machine translation, and the final version of the translation. The second instrument was the translation accuracy indicator, formulated in indicator form. The translation accuracy indicator (questionnaire) was distributed to inter-raters. The research shows that the accuracy of translation without machine translation (first version) is inaccurate (1,5); however, the accuracy of translation with machine translation (second version) is categorized as less accurate (2,4), and the translation accuracy on the final version of the translation is 2,3 (less accurate). The researcher suggests that the translator and lecturer need to use machine translation in translating, but a human touch (post-editing of translation) is really important to achieve high translation quality.

Dimensions

Plum Analytics

Author Biography

Kammer Tuahman Sipayung, University of HKBP Nommensen

English Department

References

Andari, A. Z., Sofyan, R., & Yusuf, M. (2022). The teachers’ perception towards the use of Google Translate as a translation tool. Radiant, 3(1), 1-14. https://doi.org/10.52187/rdt.v3i1.85.

Anggraini, D., Nababan, M. R., & Santosa, R. (2020). The impact of translation techniques towards the accuracy of sarcasm expression in television series the Big Bang Theory. International Journal of Multicultural and Multireligious Understanding, 7(2), 391-400. https://doi.org/10.18415/ijmmu.v7i2.1458.

Bogdan, B., & Biklen, S. K. (2007). Quality research for education: An introduction to theory and methods. London, UK: Pearson.

Chatzikoumi, E. (2020). How to evaluate machine translation: A review of automated and human metrics. Natural Language Engineering, 26(2), 137-161. https://doi.org/10.1017/S1351324919000469.

Citra T. P. (2021). Problematika penerjemahan bahasa Arab ke bahasa Indonesia menggunakan Google Translate. Seminar Nasional Bahasa Arab Mahasiswa V Tahun 2021, 5, 72-76.

Jiang, Z., Moryossef, A., Müller, M., & Ebling, S. (2022). Machine translation between spoken languages and signed languages represented in SignWriting. Computer Science: Computation and Language, 2, 1-19. https://doi.org/10.48550/arXiv.2210.05404.

Kartika, D., & Priyatmojo, A. S. (2018). Analysis of Google Translate’s quality in employing translation techniques. Journal of English Language Teaching, 7(1), 40-49. https://doi.org/10.15294/elt.v7i1.25304.

Khoiriyah, H. (2020). Kualitas hasil terjemahan Google Translate dari bahasa Arab ke bahasa Indonesia. Al Mi’yar: Jurnal Ilmiah Pembelajaran Bahasa Arab dan Kebahasaaraban, 3(1), 127-150. https://doi.org/10.35931/am.v3i1.205.

Kristina, A., & Sujarwati, I. (2021). Lexical errors produced by Google Translate in translating “Putri Serindang Bulan” to English language. Jadila: Journal of Development and Innovation in Language and Literature Education, 2(2), 200-211. https://doi.org/10.52690/jadila.v2i2.196.

Lawson, C. T., Tomchik, P., Muro, A., & Krans, E. (2019). Translation software: An alternative to transit data standards. Transportation Research Interdisciplinary Perspectives, 2, 100028. https://doi.org/10.1016/j.trip.2019.100028.

Maslihah, R. E. (2018). Akurasi penggunaan translation machine pada penulisan skripsi mahasiswa. Cendekia: Jurnal Kependidikan dan Kemasyarakatan, 16(2), 245-260. https://doi.org/10.21154/cendekia.v16i2.1295.

Nababan, M., Nuraeni, A., & Sumardiono. (2012). Pengembangan model penilaian kualitas terjemahan (Mangatur Nababan). Kajian Linguistik dan Sastra, 24(1), 39-57.

Ngo, C., Assembe, T., & Tyers, F. M. (2022). Developing a rule-based machine translation system, ewondo-french-ewondo. International Journal of Humanities and Arts Computing, 2(16), 166-181. https://doi.org/10.3366/ijhac.2022.0289.

Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2020). Assessing gender bias in machine translation: A case study with Google Translate. Neural Computing and Applications, 32(10), 6363-6381. https://doi.org/10.1007/s00521-019-04144-6.

Qian, M., Liu, J., Li, C., & Pals, L. (2019). A comparative study of English-Chinese translations of court texts by machine and human translators and the Word2Vec based similarity measure’s ability to gauge human evaluation biases. Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks. Dublin, Ireland. pp. 95-100.

Rivera-Trigueros, I. (2021). Machine translation systems and quality assessment. Language Resources and Evaluation, 56(2), 593-619. https://doi.org/10.1007/s10579-021-09537-5.

Septarina, A. A., Rahutomo, F., & Sarosa, M. (2019). Machine translation of Indonesian: A review. Communications in Science and Technology, 4(1), 12-19. https://doi.org/10.21924/cst.4.1.2019.104.

Sipayung, K. T. (2018). The impact of translation shift and method on translation accuracy found at bilingual history textbook. Jurnal Humaniora, 30(1), 58-66. https://doi.org/10.22146/jh.v30i1.27754.

Sipayung, K. T., Sianturi, N. M., Arta, I. M. D., Rohayati, Y., & Indah, D. (2021). Comparison of translation techniques by Google Translate and U-Dictionary: How differently does both machine translation tools perform in translating? Elsya: Journal of English Language Studies, 3(3), 236-245. https://doi.org/10.31849/elsya.v3i3.7517.

Sofyan, R., & Tarigan, B. (2019). Developing a holistic model of translation quality assessment. Proceedings of the Eleventh Conference on Applied Linguistics (CONAPLIN 2018): Advances in Social Science, Education and Humanities Research, 254, 266-271. http://dx.doi.org/10.2991/conaplin-18.2019.267.

Stahlberg, F. (2020). Neural machine translation: A review. Journal of Artificial Intelligence Research, 69, 343-418. https://doi.org/10.1613/JAIR.1.12007.

Stapleton, P., & Kin, B. L. K. (2019). Assessing the accuracy and teachers’ impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes, 56, 18-34. https://doi.org/10.1016/j.esp.2019.07.001.

Sujarwo, S. (2020). Students’ perceptions of using machine translation tools in the EFL classroom. Al-Lisan, 6(2), 230-241. https://doi.org/10.30603/al.v6i2.1333.

Sun, Z., Zhang, J. M., Harman, M., Papadakis, M., & Zhang, L. (2020). Automatic testing and improvement of machine translation. Proceedings - International Conference on Software Engineering. pp. 974-985. https://doi.org/10.1145/3377811.3380420.

Sutrisno, A. (2020). The accuracy and shortcomings of Google Translate translating English sentences to Indonesian. Education Quarterly Reviews, 3(4), 555-568. https://doi.org/10.31014/aior.1993.03.04.161.

Thai, K., Karpinska, M., Krishna, K., Ray, B., Inghilleri, M., Wieting, J., & Iyyer, M. (2022). Exploring document-level literary machine translation with parallel paragraphs from world literature. Computer Science: Computation and Language. https://doi.org/10.48550/arXiv.2210.14250.

Ulfiyatuzzuhriyyah, & Hilman, E. (2022). Techniques of translation of cultural words and its quality in the midnight library. Jurnal BASIS, 9(2), 269-278. https://doi.org/10.33884/basisupb.v9i2.6238.

Wang, K. (2020). Computer assisted translation with neural quality estimation and automatic post-editing. In Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020 (pp. 2175-2186). https://api.elsevier.com/content/abstract/scopus_id/85115837227.

Yaakub, M. B., Sismat, M. A. H., & Nadzirah, I. (2020). Analisis semantik dan pragmatik terhadap terjemahan mesin Google Arab-Melayu. JALL Journal of Arabic, 4, 93-106. https://doi.org/10.59202/jall.v2i2.345.

Downloads

Published

2023-10-27
Abstract 296  .
PDF downloaded 247  .