Neural Machine Translation of Balinese-Indonesian Using T5 Architecture with QLoRA Optimization - Leonard Kumaro; I Gusti Ngurah Lanang Wijayakusuma; IPW Gautama (Politeknik Negeri Batam, 2026)
This study proposes a Neural Machine Translation (NMT) system for Balinese–Indonesian translation by integrating the T5 architecture with Quantized Low-Rank Adaptation (QLoRA) to address low-resource constraints. The model is trained using the NusaTranslation dataset, consisting of 140,972 parallel sentence pairs, and optimized through parameter-efficient fine-tuning with 4-bit quantization and low-rank adaptation. Unlike conventional full fine-tuning, the proposed approach updates only a small fraction of parameters, significantly improving computational efficiency. Experimental results show that the proposed model achieves a BLEU score of 27.93%, ROUGE-1 of 18.94%, ROUGE-2 of 11.96%, ROUGE-L of 18.54%, and BERTScore F1 of 70.49%, indicating competitive performance in lexical, structural, and semantic evaluation aspects. These results demonstrate that QLoRA can maintain translation quality while reducing computational costs. Furthermore, qualitative analysis reveals that the model is capable of generating fluent and contextually appropriate translations, although challenges remain in handling complex sentence structures and linguistic variations. This study highlights the effectiveness of parameter-efficient fine-tuning for low-resource language translation and provides practical implications for developing scalable translation systems for regional languages.