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(Universitas Negeri Malang, Indonesia)(2) Aji Prasetya Wibawa
(Universitas Negeri Malang, Indonesia)(3) Leonel Hernandez
(Institucian Universitaria ITSA, Colombia, Colombia)*corresponding author
AbstractThe purpose of this research is to develop a machine translation of Bugis to Indonesian and vice versa in order to preserve the Bugis language. This research utilizes a recent dataset consisting of 30,000 Bugis-Indonesian sentence pairs from the online Bible. This research conducts scraping to compile the corpus which is then followed by manual and automatic pre-processing. The method chosen is Neural Machine Translation (NMT) while for training and testing models Long Short-Term Memory (LSTM) is used. The performance of the model is evaluated by Bilingual Evaluation Understudy (BLEU) score to measure the translation accuracy at various epochs. In addition, this study also compared the use of Adam's optimizer with non-optimizer. The results showed that the use of Adam's optimizer significantly improved the performance of the model where at epoch 2000 the model achieved the highest BLEU score of 0.996261 indicating highly accurate translation quality. In contrast, the model without the optimizer showed lower performance. Other results also found that the translation from Bugis to Indonesian was more accurate than from Indonesian to Bugis. This is due to the more balanced word count difference in the Bugis to Indonesian translation, which makes it easier for the model to match words. In conclusion, the use of NMT with Adam optimizer effectively improves the accuracy of two-way translation from Bugis-Indonesian. KeywordsBugis; Indonesian; LSTM; Machine Translation; NLP
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DOIhttps://doi.org/10.29099/ijair.v9i1.1272 |
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References
N. Hamka and N. Imansari, “The Preservation of Bugis Language in Sinjai City: A Sociolinguistic Perspective,” vol. 7, no. 1.
D. Puspitaningrum, “A Study of English-Indonesian Neural Machine Translation with Attention (Seq2Seq, ConvSeq2Seq, RNN, and MHA): A Comparative Study of NMT on English-Indonesian,” in 6th International Conference on Sustainable Information Engineering and Technology 2021, Malang Indonesia: ACM, Sep. 2021, pp. 271–280. doi: 10.1145/3479645.3479703.
T. W. Guntara, A. F. Aji, and R. E. Prasojo, “Benchmarking Multidomain English-Indonesian Machine
Translation”.
Z. Abidin, P. Permata, and F. Ariyani, “Translation of the Lampung Language Text Dialect of Nyo into the Indonesian Language with DMT and SMT Approach,” INTENSIF J. Ilm. Penelit. Dan Penerapan Teknol. Sist. Inf., vol. 5, no. 1, pp. 58–71, Feb. 2021, doi: 10.29407/intensif.v5i1.14670.
Z. Abidin, Permata, I. Ahmad, and Rusliyawati, “Effect of mono corpus quantity on statistical machine translation Indonesian – Lampung dialect of nyo,” J. Phys. Conf. Ser., vol. 1751, no. 1, p. 012036, Jan. 2021, doi: 10.1088/1742-6596/1751/1/012036.
J. Fakhrurozi, Z. Abidin, and F. Ariyani, “Prototype of Application Machine Translation Lampung Language Sentence of Api Dialect Dictionary Based Using Python Programming,” 2019.
H. Sujaini, “Improving the role of language model in statistical machine translation (Indonesian- Javanese),” Int. J. Electr. Comput. Eng. IJECE, vol. 10, no. 2, p. 2102, Apr. 2020, doi: 10.11591/ijece.v10i2.pp2102-2109.
S. H. Wijono, K. Azizah, and W. Jatmiko, “CANONICAL SEGMENTATION FOR JAVANESE- INDONESIAN NEURAL MACHINE TRANSLATION,” no. 4.
R. B. Primandhika, M. N. Munawar, and A. R. Saifullah, “Experiment on a Transformer Model Indonesian-to-Sundanese Neural Machine Translation with Sundanese Speech Level Evaluation:,” presented at the Thirteenth Conference on Applied Linguistics (CONAPLIN 2020), Bandung, Indonesia, 2021. doi: 10.2991/assehr.k.210427.069.
B. D. Wijanarko, Y. Heryadi, D. F. Murad, C. Tho, and K. Hashimoto, “Recurrent Neural Network-based Models as Bahasa Indonesia-Sundanese Language Neural Machine Translator,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Jakarta, Indonesia: IEEE, Feb. 2023, pp. 951–956. doi: 10.1109/ICCoSITE57641.2023.10127817.
Y. Heryadi, B. D. Wijanarko, D. Fitria Murad, C. Tho, and K. Hashimoto, “Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Jakarta, Indonesia: IEEE, Feb. 2023, pp. 945–950. doi: 10.1109/ICCoSITE57641.2023.10127691.
D. A. Sulistyo, “LSTM-Based Machine Translation for Madurese-Indonesian,” J. Appl. Data Sci., vol. 4,
no. 3, pp. 189–199, Sep. 2023, doi: 10.47738/jads.v4i3.113.
F. H. Rachman, Qudsiyah, and F. Solihin, “Finite State Automata Approach for Text to Speech Translation System in Indonesian-Madurese Language,” J. Phys. Conf. Ser., vol. 1569, no. 2, p. 022091, Jul. 2020, doi: 10.1088/1742-6596/1569/2/022091.
T. Apriani, H. Sujaini, and N. Safriadi, "THE EFFECT OF CORPUS QUANTITY ON THE ACCURACY OF THE WAJO BUGIS LANGUAGE STATIST TRANSLATION MACHINE TO INDONESIA," 2016.
M. Mulyana, H. Sujaini, and H. S. Pratiwi, "Algortima Division of Phrases in Sentence to Improve the Accuracy of Statistical Translation Machine of Indonesian Language - Bugis Wajo Language," J. System. And Technol. Inf. JUSTIN, vol. 6, no. 2, p. 39, Apr. 2018, doi: 10.26418/justin.v6i2.23984.
F. Stahlberg, “Neural Machine Translation: A Review,” J. Artif. Intell. Res., vol. 69, pp. 343–418, Oct. 2020, doi: 10.1613/jair.1.12007.
H. Zhang, H. Huang, and H. Han, “Attention-Based Convolution Skip Bidirectional Long Short-Term Memory Network for Speech Emotion Recognition,” IEEE Access, vol. 9, pp. 5332–5342, 2021, doi: 10.1109/ACCESS.2020.3047395.
S. J. Reddi, S. Kale, and S. Kumar, “On the Convergence of Adam and Beyond,” 2019, arXiv. doi:
48550/ARXIV.1904.09237.
R. Bawden, B. Zhang, L. Yankovskaya, A. Tättar, and M. Post, “A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing,” 2020, arXiv. doi: 10.48550/ARXIV.2004.14989.
N. Mathur, T. Baldwin, and T. Cohn, “Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics,” 2020, arXiv. doi: 10.48550/ARXIV.2006.06264.
S.-K. Im and K.-H. Chan, “Neural Machine Translation with CARU-Embedding Layer and CARU-Gated
Attention Layer,” Mathematics, vol. 12, no. 7, p. 997, Mar. 2024, doi: 10.3390/math12070997.
F. Beken Fikri, K. Oflazer, and B. Yanikoglu, “Semantic Similarity Based Evaluation for Abstractive News Summarization,” in Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Online: Association for Computational Linguistics, 2021, pp. 24–33. doi: 10.18653/v1/2021.gem-1.3.
T. Glushkova, C. Zerva, and A. F. T. Martins, “BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation,” 2023, arXiv. doi: 10.48550/ARXIV.2305.19144.
M. Agarwal, K. S. Gill, S. Malhotra, and S. Devliyal, “Analysing Digits with Sequential Convolutional Neural Networks and Adam Optimizer,” in 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), Bangalore, India: IEEE, Jun. 2024, pp. 1–4. doi: 10.1109/ICITEICS61368.2024.10625453.
M. Agarwal, K. S. Gill, S. Malhotra, and S. Devliyali, “Exploring Numerical Analysis with Sequential Convolutional Neural Networks Leveraging Adam Optimization Technique,” in 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), Nagpur, India: IEEE, Jun. 2024, pp. 1–5. doi: 10.1109/ICICET59348.2024.10616317.
W. Wang, Z. Fan, and S. H. Ng, “Trajectory-Based Multi-Objective Hyperparameter Optimization for
Model Retraining,” 2024, arXiv. doi: 10.48550/ARXIV.2405.15303.
R. Appicharla, B. Gain, S. Pal, A. Ekbal, and P. Bhattacharyya, “A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning,” 2024, arXiv. doi: 10.48550/ARXIV.2407.03076.
M. Abrishami, M. J. Rashti, and M. Naderan, “Machine Translation Using Improved Attention-based Transformer with Hybrid Input,” in 2020 6th International Conference on Web Research (ICWR), Tehran, Iran: IEEE, Apr. 2020, pp. 52–57. doi: 10.1109/ICWR49608.2020.9122317.
S. Kumar, A. Anastasopoulos, S. Wintner, and Y. Tsvetkov, “Machine Translation into Low-resource Language Varieties,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Online: Association for Computational Linguistics, 2021, pp. 110–121. doi: 10.18653/v1/2021.acl-short.16.
A. Popescu-Belis, “Context in Neural Machine Translation: A Review of Models and Evaluations,” Jan. 25, 2019, arXiv: arXiv:1901.09115. Accessed: Sep. 27, 2024. [Online]. Available: http://arxiv.org/abs/1901.09115.
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