Statistical Machine Translation Dayak Language – Indonesia Language

Authors

  • Muhammad Fiqri Khaikal Universitas Telkom
  • Arie Ardiyanti Suryani Universitas Telkom

DOI:

https://doi.org/10.30872/jim.v16i1.5315

Keywords:

Machine Translator, Statistical, Parallel Corpus, Monolingual Corpus, BLEU

Abstract

This Paper aims to discuss how to create the local language machine translation of Indonesia Language where the reason of local language selection was carried out as considering the using of machine translator for local language are still infrequently found mainly for Dayak Language machine translator.  Machine Translation on this research had used statistical approach where the resource data that was taken originated from articles on dayaknews.com pages with total parallel corpus was approximately 1000 Dayak Language – Indonesia Language furthermore as this research contains the corpus with total 1000 sentences accordingly divided into three sections in order to comprehend the certain analysis from a pattern that was created.  The monolingual corpus was collected approximately 1000 sentences of Indonesia Language.  The testing was carried out using Bilingual Evaluation Understudy (BLEU) tool and had result the highest accuracy value amounting to 49.15% which increase from some the others machine translator amounting to approximately 3%.

References

Ansori, M. S. (2019). Sosiolingustik dalam kepunahan bahasa. An-Nuha, 6(1), 52–61.

Asparilla, M. G., Sujaini, H., & Nyoto, R. D. (2018). Perbaikan Kualitas Korpus untuk Meningkatkan Kualitas Mesin Penerjemah Statistik ( Studi Kasus : Bahasa Indonesia – Jawa Krama ). 1(2), 66–74.

Darwis, H. M. (2011). The Fate of Regional Languages in the Era of Globalization: Opportunities and Challenges. 1–13.

Dugonik, J., Bošković, B., Maučec, M. S., & Brest, J. (2015). The usage of differential evolution in a statistical machine translation. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - SDE 2014: 2014 IEEE Symposium on Differential Evolution, Proceedings, December.

Hadi, I. (2014). Uji Akurasi Mesin Penerjemah Statistik (MPS) Bahasa Indonesia Ke Bahasa Melayu Sambas Dan Mesin Penerjemah Statistik (MPS) Bahasa Melayu Sambas Ke Bahasa Indonesia. Jurnal Sistem Dan Teknologi Informasi, 2, 1–6.

Mandira, S., Sujaini, H., & Putra, A. B. (2016). Perbaikan Probabilitas Lexical Model Untuk Meningkatkan Akurasi Mesin Penerjemah Statistik. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 2(1), 3–7. https://doi.org/10.26418/jp.v2i1.13393

Manual, U., & Guide, C. (2012). Statistical Machine Translation SystemUser Manual and Code Guide. University of Edinburgh, 1–267.

Mulyana, M., Sujaini, H., & Pratiwi, H. S. (2018). Algortima Pembagian Frasa Dalam Kalimat Untuk Menigkatkan Akurasi Mesin Penerjemah Statistik Bahasa Indonesia – Bahasa Bugis Wajo. Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 6(2), 39.

Sujaini, H. (2017). Meningkatkan Peran Model Bahasa dalam Mesin Penerjemah Statistik (Studi Kasus Bahasa Indonesia-Dayak Kanayatn). Khazanah Informatika: Jurnal Ilmu Komputer Dan Informatika, 3(2), 51.

Wahyuni, M., Sujaini, H., & Muhardi, H. (2019). Pengaruh Kuantitas Korpus Monolingual Terhadap Akurasi Mesin Penerjemah Statistik. Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 7(1), 20.

Wentzel, G. (1922). Funkenlinien im Röntgenspektrum. Annalen Der Physik, 371(23), 437–461.

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Published

2021-03-11