Information Correspondence on Automatic Translation (Closed Caption) of Speeches on The White House Youtube Channel

Abstrak

This study examines the information correspondence between the source and target language by analyzing the translation equivalence according to Nida and Taber’s theory. This project aims to analyze transcript data from a speech video on The White House YouTube channel. This study employs a qualitative descriptive methodology. Data was obtained using note-taking and transcription methods, then processed in three stages: reduction, presentation, and verification. Based on the findings; there are 110 data (55.8%) which are dominated by Formal equivalence, whereas 73 data (37%) are classified as Dynamic equivalence. Meanwhile, 14 data sets (7.1%) contain errors in the translation. As for the conformity of information, out of 197 data, only 14 data show errors or inconsistencies in essential areas such, proper nouns, terms, and keywords. Other translation information produced 74 data that captured meaning and emotional nuances related to the cultural context, and 109 data that maintained the structure of the translated text using a literal approach, resulting in stiff, awkward, and strange translations.