USING MACHINE TRANSLATION IN TRANSLATION MULTILANGUAGE WEBSITE: STUDY CASE OF TRANSLATING LOCAL LANGUAGE TO ENGLISH

Abstract

This research examines the use of machine translation (MT) tools in translating content from a multilanguage website, focusing on a comparison of Google Translate and DeepL. The study uses qualitative descriptive methods with a case study approach, analyzing translations from an Indonesian YouTube video titled “Ramalan Ramalan Terseram Kartun The Simpsons yang Menjadi Nyata.” The translation results from both MT tools were compared and evaluated based on Nida’s translation theory, emphasizing formal and dynamic equivalence. The findings reveal significant differences in word choices, grammar, meaning, and phrase structures between the two MT tools. DeepL demonstrated higher effectiveness in maintaining contextual accuracy and linguistic quality compared to Google Translate. Additionally, insights from a professional translator highlighted the strengths and weaknesses of manual versus automatic translation processes. This research contributes to understanding the capabilities and limitations of MT tools in translating digital content and provides practical implications for improving translation quality in multilanguage websites.