COMPARATIVE STUDY BETWEEN PAPAGO AND GOOGLE TRANSLATE QUALITY IN TRANSLATING NEWS FROM THE JAKARTA POST

The increasing reliance on machine translation (MT) systems in academic,
professional, and everyday contexts underscores the necessity for
evaluation of their effectiveness. This study investigates the quality of
translations produced by two machine translation (MT) systems, Google
Translate and Papago, using qualitative descriptive research methods. The
research assesses the translation quality based on three key indicators:
accuracy, acceptability, and readability, as proposed by Nababan (2012).
Data was collected from five selected news articles from The Jakarta Post,
which were then translated using both MT systems. The analysis involved
a systematic comparison of the translations, highlighting the three quality
aspects of each system. The findings reveal significant differences in
translation quality, with Google Translate average score of 2.72 and Papago
average score of 2.28 covering all three aspects. The study emphasizes the
importance of understanding the nuances of machine translation and its
implications for effective communication in a globalized context.
Ultimately, this research contributes to the ongoing discourse on the role of
technology in language translation and offers insights for future
improvements in MT systems