A Subtitling Analysis Of The Girl On The Train (2016) Movie

ABSTRACT GRAHITA MAHARDINA PUTRI. A320130116. A SUBTITLING ANALYSIS OF THE GIRL ON THE TRAIN (2016) MOVIE. Research Paper. Department of English and Education, Universitas Muhammadiyah Surakarta. Maret, 2017. This research focuses on subtitling strategy used by the subtitler of The Girl on the Train...

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Main Authors: Putri, Grahita Mahardina (Author), , Dr. Dwi Harjanti, M.Hum (Author)
Format: Book
Published: 2017-04.
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520 |a ABSTRACT GRAHITA MAHARDINA PUTRI. A320130116. A SUBTITLING ANALYSIS OF THE GIRL ON THE TRAIN (2016) MOVIE. Research Paper. Department of English and Education, Universitas Muhammadiyah Surakarta. Maret, 2017. This research focuses on subtitling strategy used by the subtitler of The Girl on the Train (2016) movie and subtitling quality of The Girl on the Train (2016). This research is descriptive qualitative research. The purposes of this research are: (1) to describe the subtitling strategies used by the subtitler of The Girl on the Train (2016) movie and (2) to describe the subtitling quality of The Girl on the Train (2016) movie. The data are analyzed based on the theories of subtitling strategies and subtitling quality. The data are utterances containing subtitling strategies and subtitling quality collected from the data source. The data source is the subtitle of The Girl on the Train (2016) movie. The result of this research showed that there are 929 data. The researcher finds eight strategies, as follow: (1) 213 data to paraphrase, (2) 51 data to imitation, (3) 58 to condensation, (4) 18 data to decimation, (5) 58 data to deletion, (6) 527 data to transfer, (7) 4 data to expansion and (8) 1 datum to resignation. There is no data found belonging to dislocation and transcription. There are three subtitling quality aspects should be fulfilled: accuracy, acceptability, and readability: (1) 672 data or 73.3 % accurate, (2) 232 data or 25.0 % less accurate, (3) 1 data or 0.1 % innaccurate, (4) 780 data or 83.9 % acceptable, (5) 148 data to 15.9% less acceptable, (6) 2 data or 0.2 % unnacceptable, (7) 930 data or 100 % readable. Keywords: movie, subtitling strategy, subtitling quality. 
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