AN ANALYSIS OF THE TRANSLATION OF TWITTER HELPING CENTER WEBSITE WWW.SUPPORT.TWITTER.COM

ABSTRACT Hani Iswuri. C0312032. An Analysis of the Translation of Twitter Helping Center Website www.support.twitter.com. Thesis. English Department. Faculty of Cultural Sciences. Sebelas Maret University. This research focuses on analysis of translation techniques applied in translating Twitter Helping Center website and the quality of translation in terms of accuracy, acceptability and readability. The purposes of this research are: (1) to identify the translation techniques applied by the translator in translating the expressions found in Twitter Helping Center website; and (2) to find out the impact of the use of translation techniques towards the quality of the translation in terms of accuracy, acceptability, and readability. This research was descriptive-qualitative research by using purposive sampling technique. The data are the expressions found in the website of Twitter Helping Center www.support.twitter.com. The data source of this research is the the website of Twitter Helping Center www.support.twitter.com. The total data found in the website are 100 data in the form of expressions; words and sentences which then analyzed per the smallest unit using the techniques proposed by Molina&Albir and resulting to the total of 426 data. The analysis on the translation technique shows that there are 9 single techniques and 12 multiple techniques applied by the translator in translating the website of Twitter Helping Center. The single techniques are: Established Equivalent (290 data), Borrowing (61 data), Variation (18 data), Amplification (11 data), Modulation (8 data), Discursive Creation (5 data), Transposition (4 data), Reduction (2 data) and Description (1 data). While the multiple techniques are: Amplification + Established Equivalent (6 data), Reduction + Established Equivalent (6 data), Borrowing + Established Equivalent (3 data), Modulation + Established Equivalent (2 data), Modulation + Borrowing (2 data), Borrowing + Amplification (1 data), Borrowing + Reduction (1 data), Transposition + Borrowing (1 data), Modulation + Transposition (1 data), Reduction + Borrowing (1 data), Modulation + Borrowing + Established Equivalent (1 data), Modulation + Established Equivalent + Discursive Creation (1 data) The research findings indicate that the techniques which produce the high level of accuracy in translation of Twitter Helping Center website are established equivalent and borrowing technique. Meanwhile, the techniques which produce the low level of accuracy in translation of Twitter Helping Center website are reduction and discursive creation. In terms of acceptability, the use of established equivalent technique gives positive effect. It happens because the translator uses equivalent terms in Indonesian which are familiar and commonly used by the readers. Meanwhile, the technique which gives negative effect to the level of acceptability of the data is borrowing technique. Regarding to readability, the technique which produce high level of readability is established equivalent, while the technique which produce low level of readability in this research are borrowing and discursive creation. Keywords: Twitter, localization, website translation, translation technique, translation quality, accuracy, acceptability, readabilit

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