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All Journal Journal of Research on English and Language Learning (J-REaLL) Journal of Language and Literature Studies
Syafiee, Amieziezaitul Syazlien Ezzeq Ezrynah Amirul
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Education Languange, Linguistic, Communication & Media Social Sciences Other

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A comparative analysis of Google Translate and ChatGPT in translating Borneo Bulletin News into standard Malay Syafiee, Amieziezaitul Syazlien Ezzeq Ezrynah Amirul; Yaqin, Lalu Nurul
Journal of Research on English and Language Learning (J-REaLL) Vol. 6 No. 1 (2025): Journal of Research on English and Language Learning (J-REaLL)
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/j-reall.v6i1.23252

Abstract

In the digital era, machine translation tools like Google Translate and ChatGPT have become essential for cross-linguistic communication, particularly in translating formal texts such as news articles. This study aims to evaluate the effectiveness of these tools in translating English news articles from Borneo Bulletin into Standard Malay, focusing on lexical accuracy, syntactic structure, and contextual appropriateness. A qualitative comparative analysis was conducted by translating selected articles using both Google Translate and ChatGPT-4. The outputs were manually analyzed using a qualitative rubric guided by meaning-based translation theory, focusing on word choice, sentence structure, the original tone, and cultural nuances. The results indicate that ChatGPT-4 outperforms Google Translate in maintaining lexical precision, grammatical coherence, and contextual relevance. ChatGPT-4 demonstrated superior handling of idiomatic expressions and complex sentence constructions, producing translations that adhered closely to journalistic norms in Standard Malay. However, both tools exhibited limitations in accurately translating culturally specific references and specialized terminology. Google Translate, despite improvements through neural machine translation, tended to produce overly literal translations, leading to a loss of tone and clarity in formal contexts. These findings highlight the potential and limitations of machine translation in journalism, where accuracy and tone are crucial for public trust and information dissemination. The study emphasizes the necessity for enhanced machine translation algorithms and the integration of human feedback to elevate translation quality in low-resource languages such as Malay.
Syntactic Challenges in ChatGPT-5’s Translation of English News Texts into Standard Malay: A Generative Transformation Model Syafiee, Amieziezaitul Syazlien Ezzeq Ezrynah Amirul; Yaqin, Lalu Nurul
Journal of Language and Literature Studies Vol. 6 No. 2 (2026): June
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jolls.v6i2.4590

Abstract

The growing use of AI translation has increased the need to evaluate whether generative models can produce grammatically accurate translations for underrepresented languages such as Malay. English-to-Malay translation remains challenging because Malay syntax requires accurate verb affixation, modifier placement, phrase ordering, and noun phrase + verb phrase (FN + FK) alignment in Standard Malay. This study examines the syntactic and lexical accuracy of ChatGPT-5 in translating English news texts into Standard Malay, with a focus on FN + FK structures. Grounded in Generative Transformation Theory and Nik Safiah Karim’s Malay grammar framework, the research analyzes how underlying English sentence structures are transformed into Malay surface structures. Using a qualitative descriptive linguistic design supported by descriptive error analysis, authentic bilingual sentences from the Borneo Bulletin were analyzed across social, economic, technological, cultural, and sports domains. The samples were selected from news sentences containing FN + FK structures, translated using a standardized ChatGPT-5 prompt, coded according to syntactic error categories, and validated through review by two qualified linguists and Malay grammar specialists. Findings show that ChatGPT-5 generally preserves complex sentence structures, including verb affixation, modifier placement, and phrase order, while accurately translating culturally embedded expressions and technical terms, such as mock cheque, Turnaround, and king of fruits. Minor syntactic deviations were observed, particularly in morphological mapping, modifier sequencing, and lexical narrowing, but they did not significantly affect the meaning. The study demonstrates ChatGPT-5’s potential as a supportive AI tool for multilingual translation and highlights the value of syntactically informed evaluation. These insights inform both the theoretical understanding of Malay syntax in AI translation and practical applications for professional, academic, and media contexts.
Co-Authors Yaqin, Lalu Nurul