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Patterns of Cultural Transplantation in the English Translation of Goenawan Mohamad’s Poem “Pastoral” Wiraswastiningrum, Ni Wayan Swardhani; Junining, Esti; Yulianto, Wawan Eko
Alphabet Vol. 9 No. 01 (2026)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.alphabet.2026.09.01.05

Abstract

This study examines the translation of cultural terms in the poem “Pastoral,” focusing on strategies used to achieve clarity and comprehension in the target language (TL). The findings show that cultural transplantation is primarily realized through adaptation as the dominant strategy, followed by addition and reduction. These strategies reflect the translator’s effort to balance fidelity to the source text (ST) with accessibility for target readers. Although some cultural nuances may be reduced, these procedures enhance readability and support clearer understanding of meaning. Cultural transplantation thus serves as a key approach in bridging cultural differences, enabling the themes and emotional depth of the original poem to be conveyed effectively in the target text (TT). The study also highlights the role of translation in facilitating coherent intercultural communication. Future research is recommended to apply comparative analyses across literary texts to identify patterns in translating cultural terms and deepen insights into the relationship between language, culture, and translation.
Enhancing the Quality of English–Indonesian Subtitle Translation Using Prompt Engineering in ChatGPT Gigih Bella Wicaksono; Wawan Eko Yulianto; Esti Junining
Jurnal Bahasa Inggris Terapan Vol. 12 No. 1 (2026): April 2026 [IN PROGRESS]
Publisher : Jurusan Bahasa Inggris - Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/jbit.v12i1.6823

Abstract

Constraints and guidelines on subtitle translation impose demanding tasks on machine translation systems, hence the urge to control translation output from machine translators. Unlike conventional machine translation, text-generating artificial intelligence such as ChatGPT can produce controlled translations that satisfy subtitle translation guidelines. Thus, this study aims to maintain the quality of subtitle translation while limiting character length to keep subtitles compact. The study employed a descriptive-comparative analysis to examine the differences between a plain prompt and a properly designed prompt. The prompts used in this study included details such as contextual information, target audience specifications, formal language requirements, character-length limitations, prohibition on altering sentence order, and illustrative examples. This study utilized 50 subtitle segments from the film Mufasa: The Lion King (2024) and implemented four prompt types: Plain Prompt, Zero-Shot Prompt, One-Shot Prompt, and Few-Shot Prompt. Translation quality was measured using COMET, BERTScore, and chrF++. Scores from these metrics showed improvement, with the Few-Shot Prompt reaching 89.920, 88.830, and 59.904, respectively, while the Plain Prompt yielded only 89.000, 85.806, and 55.767. The findings also revealed a reduction in character count, with the Plain Prompt generating 1,834 characters and the Few-Shot Prompt generating 1,377. These results indicate that the Few-Shot Prompt produces higher-quality translations and maintains greater compactness compared to the Plain Prompt.