Lexical Errors Made by Instagram Machine Translation in Translating the Account of “CNN Indonesia” News Article

Abstract

This study aims to depict the types of errors made by Instagram Machine Translation and to find out the most dominant types of lexical errors made by Instagram Machine Translation on ‘CNN Indonesia’ Instagram account. The design of this research was qualitative design. The data were in words, phrases, and sentences that contained lexical errors made by Instagram Machine Translation on the “CNNIndonesia” Instagram account. The data were taken by running an Instagram in one account of various captions related to the lexical errors of the study object. The data were collected through stages: finding out and determining, classifying, and separating the words, phrases, and sentences that contained lexical errors made Instagram Machine Translation on the “CNN Indonesia” Instagram account. The techniques of analysis data researcher translated the captions using Instagram machine translation and then the translation result is compared to the source language. The next step is to examine the lexical errors produced by Instagram machine translation. The research result shows that the types of lexical errors made by Instagram Machine Translation on the “CNN Indonesia” Instagram account based on the error categories theory by Vilar et al founds are: 4 missing errors, 10 incorrect words, and 8 unknown words. All errors indicated that Instagram machine translation could not represent the target language in the “CNN Indonesia” Instagram account. The users of Instagram need to filter every translation that is translated by Instagram machine translation before receiving it as information.