A Comparative Study of Google Translate and DeepL in Translating Indonesian to English Among Informatics Engineering Students at Universitas Potensi Utama Medan

Abstract

This study aims to compare the accuracy and effectiveness of Google Translate and DeepL in translating Indonesian texts into English, focusing on Informatics Engineering students at Universitas Potensi Utama. In an era where machine translation tools have become essential in academic and professional settings, understanding their strengths and limitations is crucial. Grounded in Newmark’s translation theory and supported by insights from neural machine translation research, this study employs a mixed-method approach, combining quantitative scoring of translation outputs and qualitative analysis of linguistic accuracy, context retention, and semantic coherence. The research findings reveal that DeepL generally outperforms Google Translate in preserving contextual meaning and producing grammatically accurate sentences, although Google Translate remains more familiar and accessible to students. The statistical analysis shows a significant difference in translation quality between the two tools (p < 0.05), while student interviews highlight DeepL's advantage in handling nuanced or academic texts. This research provides valuable insights for educators and learners in choosing appropriate translation tools, and offers implications for the integration of machine translation in language learning and academic writing.