Translation of the Lampung Language Text Dialect of Nyo into the Indonesian Language with DMT and SMT Approach
DOI:
https://doi.org/10.29407/intensif.v5i1.14670Keywords:
Lampung language dialect of Nyo, Direct machine translation, Statistical machine translation, Bilingual Evaluation UnderstudyAbstract
Research on the translation of Lampung language text dialect of Nyo into Indonesian is done with two approaches, namely Direct Machine Translation (DMT) and Statistical Machine Translation (SMT). This research experiment was conducted as a preliminary effort in helping students immigrants in the province of Lampung, translating the Lampung language dialect of Nyo through prototypes or models was built. In the DMT approach, the dictionary is used as the primary tool. In contrast, in SMT, the parallel corpus of Lampung Nyo and Indonesian language is used to make language models and translation models using Moses Decoder. The result of text translation accuracy with the DMT approach is 39.32%, and for the SMT approach is 59.85%. Both approaches use Bilingual Evaluation Understudy (BLEU) assessment.
Downloads
References
F. Ariyani, “Distribusi Verba Berfrefiks (N-) Pada Bahasa Lampung dalam Kitab Kuntara Raja Niti dan Buku Ajar. Ranah: Jurnal Kajian Bahasa 3,” Ranah J. Kaji. Bhs., vol. 3, no. 2, pp. 124–134, 2014, doi: https://doi.org/10.26499/rnh.v3i2.43.
P. Bhattacharyya, Machine Translation. Boca Raton: Taylor & Francis Group, 2015.
Z. Abidin, “Penerapan Neural Machine Translation untuk Eksperimen Penerjemahan secara Otomatis pada Bahasa Lampung – Indonesia,” Pros. Semin. Nas. Metod. Kuantitatif 2017, no. 978, pp. 53–68, 2017.
Z. Abidin, A. Sucipto, and A. Budiman, “Penerjemahan Kalimat Bahasa Lampung-Indonesia Dengan Pendekatan Neural Machine Translation Berbasis Attention Translation of Sentence Lampung-Indonesian Languages With Neural Machine Translation Attention Based,” J. Kelitbangan, vol. 06, no. 02, pp. 191–206, 2018.
P. Permata and Z. Abidin, “Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia,” Media Inform. Budidarma, vol. 4, no. 3, pp. 519–528, 2020, doi: 10.30865/mib.v4i3.2116.
S. Ningsih and S. Saniati, “Eksperimen Pengenalan Ucapan Aksara Lampung Dengan CMU Sphinx 4,” J. Teknoinfo, vol. 12, no. 1, p. 33, 2018, doi: 10.33365/jti.v12i1.40.
D. V Sindhu and B. M. Sagar, “Dictionary Based Machine Translation from Kannada to Telugu,” IOP Conf. Ser. Mater. Sci. Eng., vol. 225, p. 012182, 2017, doi: 10.1088/1757-899x/225/1/012182.
R. M. M. Shalini and B. Hettige, “Dictionary Based Machine Translation System for Pali to Sinhala,” Sri Lanka, no. October 2017, p. 6, 2017.
K. T. C. Resmawan, I. K. R. Arthana, and I. M. G. Sunarya, “Pengembangan Aplikasi Kamus Dan Penerjemah Bahasa Indonesia–Bahasa Bali Menggunakan Metode Rule Based Berbasis Android,” KARMAPATI (Kumpulan Artik. Mhs. Pendidik. Tek. Inform., vol. 4, no. 2, pp. 70–81, 2015.
N. Afifah, T. B. Santoso, and M. Yuliana, “Pembuatan Kamus Elektronik Kalimat Bahasa Indonesia dan Bahasa Jawa untuk Aplikasi Mobile Menggunakan Interpolation Search,” Semin. Proy. Akhir Jur. Tek. Telekomun. PENS-ITS, pp. 1–7, 2010.
R. Nugroho Aditya, T. Adji Bharata, and B. Hantono S, “Penerjemahan Bahasa Indonesia dan Bahasa Jawa Menggunakan Metode Statistik Berbasis Frasa,” Semin. Nas. Teknol. Inf. dan Komun., vol. 2015, no. Sentika, 2015.
A. A. Suryani, D. H. Widyantoro, A. Purwarianti, and Y. Sudaryat, “Experiment on a phrase-based statistical machine translation using PoS Tag information for Sundanese into Indonesian,” 2015 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2015 - Proc., 2016, doi: 10.1109/ICITSI.2015.7437678.
Y. Jarob, H. Sujaini, and N. Safriadi, “Uji Akurasi Penerjemahan Bahasa Indonesia – Dayak Taman Dengan Penandaan Kata Dasar Dan Imbuhan,” J. Edukasi dan Penelit. Inform., vol. 2, no. 2, pp. 78–83, 2016, doi: 10.26418/jp.v2i2.16520.
H. Sujaini, “Meningkatkan Peran Model Bahasa dalam Mesin Penerjemah Statistik (Studi Kasus Bahasa Indonesia-Dayak Kanayatn),” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 3, no. 2, p. 51, 2017, doi: 10.23917/khif.v3i2.4398.
T. Apriani, H. Sujaini, and N. Safriadi, “Pengaruh Kuantitas Korpus Terhadap Akurasi Mesin Penerjemah Statistik Bahasa Bugis Wajo Ke Bahasa Indonesia,” J. Sist. dan Teknol. Inf., vol. 1, no. 1, pp. 1–6, 2016.
C. Adiputra, Krisna and Y. Arase, “Performance of Japanese-to-Indonesian Machine Translation on Different Models,” Proc. 23rd Annu. Meet. Linguist. Process. Soc., no. C, pp. 7–10, 2017.
M. A. Sulaeman and A. Purwarianti, “Development of Indonesian-Japanese statistical machine translation using lemma translation and additional post-process,” Proc. - 5th Int. Conf. Electr. Eng. Informatics Bridg. Knowl. between Acad. Ind. Community, ICEEI 2015, no. i, pp. 54–58, 2015, doi: 10.1109/ICEEI.2015.7352469.
S. Mandira, H. Sujaini, and A. B. Putra, “Perbaikan Probabilitas Lexical Model Untuk Meningkatkan Akurasi Mesin Penerjemah Statistik,” J. Edukasi dan Penelit. Inform., vol. 2, no. 1, pp. 3–7, 2016, doi: 10.26418/jp.v2i1.13393.
D. W. Ningtyas, H. Sujaini, and N. Safriadi, “Penggunaan Pivot Language pada Mesin Penerjemah Statistik Bahasa Inggris ke Bahasa Melayu Sambas,” J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 173, 2018, doi: 10.26418/jp.v4i2.27414.
M. D. Etsa, H. Sujaini, and N. Safriadi, “Pengaruh Metode Dictionary Lookup pada Cleaning Korpus Terhadap Akurasi Mesin Penerjemah Statistik Indonesia–Melayu Pontianak,” J. Edukasi dan Penelit. Inform., vol. 4, no. 1, p. 49, 2018, doi: 10.26418/jp.v4i1.24595.
H. Priyatman, Hendro; Saleh, Muhammad ; Sujaini, “Analisis Akurasi Algoritma Extended Word Similarity Based Clustering ( EWSB ) pada Mesin,” J. Edukasi dan Penelit. Inform., vol. 6, no. 3, pp. 323–327, 2020.
H. Sujaini and R. D. Nyoto, “Perbandingan Nilai Akurasi Algoritma Smoothing pada Mesin Penerjemah Statistik Bahasa Indonesia ke Bahasa Melayu Sambas dengan Language Model Toolkit IRSTLM,” J. Edukasi dan Penelit. Inform., vol. 6, no. 3, pp. 291–300, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. 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 acknowledgment of its initial publication in this journal.
4. 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.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License