THE QUALITY OF MACHINE TRANSLATION IN TWITTER “ISLAMIC THINKING” ACCOUNT

Ari Pradina, Yesika and Undari, Lilik (2018) THE QUALITY OF MACHINE TRANSLATION IN TWITTER “ISLAMIC THINKING” ACCOUNT. skripsi thesis, Institut Agama Islam Negeri Surakarta.

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Abstract

Yesika Ari Pradina. 2017. The Quality of Machine Translation in Twitter Islamic Thinking Account. Thesis. English Letters Study Program, Islamic Education and Teacher Training Faculty. Advisors : Hj. Lilik Untari, S.Pd. M.Hum Key words : Machine Translation, Translation Quality Assessment, Twitter, Islamic Thinking The researcher analyze The Quality of Machine Translation in Twitter Islamic thinking Account. In this research has three problem statement which are : translation accuracy on machine translation in twitter, translation acceptability on machine translation in twitter, and translation readadiability on machine translation in twitter. The purpose of this research is to know what the result of machine translation in twitter is accurate and acceptable. In this research the researcher used descriptive qualitative method. The data took from account twitter islamic thinking and observation. The other data are taken questionnaries assessed by rater and respondents. The limitation of the data in this research are the tweet / the posting with periode february until april 2016. The technique of data collection is documentation and questionnaries. The technique of data analysis is collecting data, data reduction, and data display. In this research uses nababan theories about translation quality assessment. The research findings 80 data screenshot. The dominant tweet in this translation is complex sentences. In category accuracy the researcher conclude there are 17 data (21.25%) as accurate, 30 data (37.75 %) as less accurate and 33 data (41.25 %) as inaccurate. . In category acceptability the researcher conclude there are 20 data (25%) as acceptable, 29 data (36.25%) as less acceptable and 31 data (38.75%) as unacceptable. In category readadiability the researcher conclude there are 20 data (25%) as readiable, 29 data (36.25%) as less readiable and 31 data (38.75%) as unreadiable.

Item Type: Thesis (skripsi)
Subjects: 800 Sastra > 820 English and Old English literatures > 826 English letters
Divisions: Fakultas Ilmu Tarbiyah dan Keguruan > Sastra Inggris
Depositing User: Maha siswa
Date Deposited: 10 Dec 2018 02:18
Last Modified: 10 Dec 2018 02:18
URI: http://eprints.iain-surakarta.ac.id/id/eprint/2032

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