Unveiling Semantic Errors Found in Lexical Translations of Tasya Farasya’s Tiktok Account
DOI:
https://doi.org/10.21512/lc.v17i2.10435Keywords:
semantic errors, lexical translation, TikTok machine translationAbstract
The research observed the semantic errors in lexis that occurred in the translation by TikTok machine translation. It became the main issue in translation studies because the accuracy of translation produced by machine translation was still questionable and debatable. The research aimed to identify the types of semantic error in lexis made by TikTok auto machine translation found in Tasya Farasya TikTok’s account and suggested a more appropriate translation. The research applied a descriptive qualitative method to analyze the error in translation produced by TikTok machine translation. The theory proposed by Sayogie (2014) was used to classify the data based on semantic aspects: grammatical meaning, contextual meaning, and referential meaning. The research results show that three types of errors are found, and the most frequent error found is an error in contextual meaning. TikTok machine translation is incapable of translating accurately because it does not know the context of the situation and translates it literally. Based on research findings, TikTok users cannot entirely rely on machine translation because it still has weaknesses in translating several terms. Thus, it is highly important that TikTok should evaluate and improve the quality of the machine translation.
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