Abstract
In this study, the writer discusses common errors done by YouTube Auto-Translate in translating some of movie trailers with Indonesian subtitle as the Target Language (TL). Then, the writer compares the subtitles in the target language to that of a professional translation’s output and YouTube's Auto-Translate output and categorizes the errors by referring to the error classification by Vilar et al. The writer found 14 errors on YouTube Auto-Translate output. After observing the 14 data, it showed that commonly errors found from the data are related to lexical level by nine times (63%). The second error type is related to disambiguation which took place four times (27%). The errors found with the lowest frequency are Word Order and Unknown Word with which each of them is only shown once (5%). To sum up, machine translation helps us a lot in looking up words in a dictionary, but it is not recommended to rely on machine translation as it fails to recognize the context. It is then suggested that whenever we use MT, we must have it post-edit by human translator.
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