<![CDATA[This research examines the application of Nida’s (1964) formal equivalence theory in machine translation, with a focus on the structural and grammatical aspects of CNN headlines translated by Bing Translator and DeepL. This research uses a descriptive qualitative approach, analyzing translated headlines related to the 2024 United States Election. Data was collected from CNN’s official website from October to November 2024, and translations were obtained from both machine translation tools. The analysis shows that whereas both translation tools attempt to maintain structural accuracy, Bing Translator tends to preserve rigid sentence structures, while DeepL adapts phrases to improve fluency, sometimes at the expense of grammatical accuracy. A readability assessment, based on the researcher’s interpretation, shows that DeepL produces smoother translations, which are more accessible to readers. This research contributes to the machine translation reliability discussion in journalism and emphasizes the challenge of maintaining formal equivalence while ensuring readability. Future research can explore a broader corpus or integrate computational linguistic analysis to improve translation evaluation methodology.Keywords: Formal Equivalence; Machine Translation; News Translation; Readability; News Headlines]]>
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