News has been spread internationally since it was digitalized. This situation makes machine translation used as a tool to solve the language barrier problem, as it is cheap and fast compared to human translators. However, the translation by Machine Translation is not always correct. In fact, it results in more problems than in successful translation; in other words, the use of this machine is like ‘garbage in, garbage out’. However, not many studies have been conducted to provide evidence of the weaknesses of machine translation. This research paper attempts to discover the translation methods and procedures of the “translate to Indonesian” featured by Google Translate in the translation of CNN International current news from English to Indonesian. The data consist of 10 pieces of entertainment news that are published online on CNN International News. A descriptive-qualitative approach is used to analyze the data. The scope of the analysis is lexical words only. The translated news was observed and compared to the original news in order to identify the methods and procedures applied in the translation results by the Machine Translation. The results of this analysis reveal that the “translate to Indonesian” feature from Google Translate commonly uses the literal and faithful translation methods and the procedure mostly found is borrowing. Consequently, the translation by this machine is still awkward and requires substantial improvement.

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