Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia

Authors

  • Permata Permata Universitas Teknokrat Indonesia, Bandar Lampung
  • Zaenal Abidin Universitas Teknokrat Indonesia, Bandar Lampung

DOI:

https://doi.org/10.30865/mib.v4i3.2116

Keywords:

Paralel Corpus, Mono Corpus, Statistical Machine Translation, Lampung Language, BLEU

Abstract

In this research, automatic translation of the Lampung dialect into Indonesian was carried out using the statistical machine translation (SMT) approach. Translation of the Lampung language to Indonesian can be done by using a dictionary. Another alternative is to use the Lampung parallel body corpus and its translation in Indonesian with the SMT approach. The SMT approach is carried out in several phases. Starting from the pre-processing phase which is the initial stage to prepare a parallel corpus. Then proceed with the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The results of testing the translation of Lampung sentences into Indonesian shows the accuracy of the Bilingual Evaluation Undestudy (BLEU) obtained is 77.07% in 25 single sentences without out-of-vocabulary (OOV), 72.29% in 25 single sentences with OOV, 79.84% at 25 compound sentences without OOV and 80.84% at 25 compound sentences with OOV.

Author Biographies

Permata Permata, Universitas Teknokrat Indonesia, Bandar Lampung

1Fakultas Teknik dan Ilmu Komputer, Prodi Informatika

Zaenal Abidin, Universitas Teknokrat Indonesia, Bandar Lampung

2Fakultas Teknik dan Ilmu Komputer, Prodi Sistem Informasi

References

L. Post, “Nestapa Guru Bahasa Lampung,†Lampung Post, Bandar Lampung, Jul. 2017.

Antara, “139 Bahasa Daerah di Indonesia Terancam Punah,†Republika.co.id, Bandung, Aug. 2016.

P. Bhattacharyya, Machine Translation. Boca Raton: Taylor & Francis Group, 2015.

A. Maslan, Y. Setiono, and F. Alfazri, “Pengembangan Smart Application Translation Aneka Bahasa Sulawesi Berbasis Android,†TEKNOSI, vol. 02, no. 01, pp. 55–64, 2016.

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.

F. Rohman, P. W. Buana, A. Agung, and K. Wiranata, “Rancang Bangun Penerjemah Bahasa Indonesia ke Bahasa Jawa Berbasis Android,†J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), vol. 3, no. 1, pp. 40–47, 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.

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.

R. M. M. Shalini and B. Hettige, “Dictionary Based Machine Translation System for Pali to Sinhala,†Sri Lanka, no. October 2017, p. 6, 2017.

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.

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.

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.

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.

H. Tanuwijaya and H. Manurung, Maruli, “Penerjemah Dokumen Inggris-Indonesia Menggunakan Mesin Penerjemah Statistik Dengan Word Reordering dan Phrase Reordering,†J. Ilmu Komput. dan Inf., vol. 2, no. 1, pp. 17–24, 2009.

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.

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.

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: a Method for Automatic Evaluation of Machine Translation,†Proc. 40th Annu. Meet. Assoc. Comput. Linguist., pp. 311–318, 2002, doi: 10.1002/andp.19223712302.

Megaria, “Afiks Pembentuk Adjektiva dalam Bahasa Lampung Dialek A Logat Belalau (Analisis Morfologis),†J. LOKABASA, vol. 4, no. 2, pp. 195–201, 2013.

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Published

2020-07-20

Issue

Vol. 4 No. 3 (2020): Juli 2020

Section

Articles

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