Enhancing the Translation Quality of ChatGPT in Educational News: A Technique-Based Study on Adengad.Net

Abstract

The purpose of this study is to describe the translation techniques used in translating educational news on Adengad.Net as an effort to improve the quality of ChatGPT translation results. Along with the development of technology and the increasing need for accurate and efficient translation. The method used is qualitative method by using the theory of translation techniques by Molina Albir and the theory of translation quality by Nababan. This study evaluates the improvement of translation quality, context accuracy, as well as text readability and acceptability. The analysis of 10 education news on Adengad.Net channel found 8 out of 18 translation techniques classified by Molina and Albir, with literal equivalence and modulation being the most frequently used techniques at 31 and 22 times respectively. The evaluation of translation quality involving three students of the Tarjamah study program recorded an accuracy score of 2.53, acceptability of 2.37, and readability of 2.47, with a final weighted average of 2.47. The findings show that the translations produced by ChatGPT are generally accurate, acceptable and comprehensible. This study can broaden the knowledge of the application of translation techniques in visual media and provide practical guidance for translation practitioners and enthusiasts.

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