The Analysis of Translation Error from Indonesian Source Text to English Target Text by Tiktok Automatic Machine Translation

Abstract

This study focuses on the phenomenon of translation error in Tiktok automatic machine translation. This study applies a qualitative method. In addition, the source of data for this study is 25 caption text from Natasha Surya's Tiktok account, which consists of words and phrases. This research classifies and analyzes the types of translation errors found in Surya's Tiktok caption text using the classification of translation errors by Vilar, Xu, D'Haro, and Ney (2006). The classification consists 15 types of translation errors, and 8 of them were identified in this study, and also provide suggested revisions for ST and suggested translations for ST. The results of this study show that 35 translation errors occur in Surya's Tiktok caption text. There are unknown stem words, missing content words, wrong lexical choices, short-range words, level word order, extra words, incorrect form, idioms, and long-range words, level word order. The top three translation errors often found in this study are 15 unknown stem words, 7 missing content words, and 3 wrong lexical choice.