Students’ Opinion in Using DEEPL Application as A Machine Translation Tool in Translation Class (A Study of English Department Students of UIN Mahmud Yunus Batusangkar)

Abstract

Fajar Gustian Putra, Reg. No. 2130104022, entitled: “Students' Opinion in Using DeepL Application as a Machine Translation Tool in Translation Class” (A Study of English Department Students of UIN Mahmud Yunus Batusangkar). English Teaching Department Faculty of Tarbiyah and Teacher Training Islamic State University of Mahmud Yunus Batusangkar. This study is motivated by the need to further understand students' opinions on the benefits and challenges of using DeepL in Translation classes due to its widespread use and its reputation for good accuracy in translating texts. Therefore, this study aims to explore students' opinions on the use of DeepL, including its advantages, constraints, and impact on their translation skills. This study used a qualitative method with a descriptive design, with a saturation sampling technique used to determine informants, in which data were collected until reaching the point of saturation, 38 students reported using DeepL. Of these, 4 students were selected as the main informants based on the completeness of the information provided until no new data emerged. Data were collected through in-depth interviews and analyzed using data reduction, data presentation, and verification techniques to increase the validity of the research results. The findings reveal there are seven student opinions on the use of the DeepL application: (1) DeepL is considered highly accurate and reliable for academic texts, particularly journal articles and formal documents, (2) DeepL is considered efficient and easy to use, allowing students to complete translation tasks more quickly and conveniently, (3) DeepL helps students understand sentence structure and expand their vocabulary, particularly through the synonym suggestion feature, (4) the PDF translation feature of DeepL is very beneficial to students, allowing them to translate academic materials efficiently, (5) DeepL's translation results are considered less suitable in translating idioms and cultural terms, which often require manual revision to ensure accuracy, (6) while DeepL is effective for formal texts, it produces less natural translations for conversational or creative writing, and (7) DeepL is considered limiting in translating certain languages due to its more limited language availability compared to other translation tools..