1 ELT FORUM 5 (2) (2016) Journal of English Language Teaching http://journal.unnes.ac.id/sju/index.php/elt ERRORS MADE IN GOOGLE TRANSLATE IN THE INDONESIAN TO ENGLISH TRANSLATIONS OF NEWS ITEM TEXTS Adam Ismail Dr. Rudi Hartono, S.S., M.Pd. English Department. Faculty of Languages and Arts. State University of Semarang. Aticle Info ________________ Article History: Received in April 2016 Approved in April 2016 Published in May 2016 ________________ Keywords: Translation errors, Google Translate, News Item texts ____________________ Abstract ___________________________________________________________________ The objective of this study was to describe the errors made in the Indonesian to English Google Translate translations of News Item texts. This study employed five English translations of Indonesian News Item texts taken from (1) bbc.com, (2) kompas.com, (3) liputan6.com, (4) tempo.co, and (5) viva.co.id made by using Google Translate. Those samples were taken by using purposive sampling technique. The categorization of translation errors were adapted from Dewi’s Indonesian-English translation error typology. The errors can occur in the words, phrases, clauses, and sentences of the texts. The data gathered were analyzed descriptively by using qualitative approach. The findings of the analysis showed that there were 13 categories of translation errors obtained from 278 data. Ranking from the most common to the least common errors found in the data, they were grammatical errors (24.46%), terminology errors (15.47%), omission errors (14.03%), syntax errors (12.23%), mistranslation/misunderstanding errors (6.47%), literalness/faithfulness errors (6.47%), usage errors (6.12%), punctuation errors (3.60%), addition errors (2.88%), ambiguity errors (2.52%), word form errors (2.52%), capitalization errors (2.16%), and spelling errors (1.08%). The data findings also showed that statistical method of Google Translate, the inability of Google Translate to understand the contexts of the texts beyond sentence, and errors in the source texts that carried into translation errors in the target texts were the causes of translation errors found in the data. © 2016 Universitas Negeri Semarang  Correspondent Address: B3 Building FBS Unnes Sekaran, Gunungpati, Semarang, 50229 E-mail: adamfrenzy@gmail.com ISSN 2252-6706 Adam Ismail / Journal of English Language Teaching 5 (2) (2016) 2 INTRODUCTION Background of the Study Nowadays, English is considered as international language due to the impact of globalization. In the process of understanding English, translation is needed. According to Catford (1965:20), translation is the replacement of textual material in one language (SL) by equivalent textual material in another language (TL). Its uses can be found in the translation of textbooks, state documents, literary works, bilingual books, business documents, journals or scientific works, and so forth. Hence, because of its vital role, translation can offer a solution to overcome language gap in communication. Indonesian people often use internet in their daily life. One of the tools on the internet that can help them in the translation process is Google Translate service. Google Translate is a convenient tool that offers free instant translation service on the web. It can be utilized to translate words, clauses, sentences, paragraphs, and even a web page between any pairs of supported languages. Moreover, it can also be utilized to minimize time and effort to do translation tasks because the translation results are instantly generated. The translator is also helped with the easiness and availability of Google Translate, which are online and accessible to anyone and anytime for free with internet connection. Meanwhile, the use of Google Translate to translate has brought some issues. Some translators might use Google Translate blatantly without any revising effort on its translation, which leads to some underwhelming translation results. Google Translate itself also has limitation, that when translating complex sentences, it would sometimes resulted in inaccurate translation. There is an opinion that using Google Translate to do the translation work is too narrow-minded and easy because anyone can copy the text to the Google translate, choose the language, and press the translate button. Even, anyone who does not have any proficiency in both source and target language is able to do it. Despite of those issues, the use of Google Translate to do the translation works is indeed fast and can bring an instant overview of translation result. From those issues, there comes an idea of analyzing the errors in translation results of certain type of text by using Google Translate. One type of text, which is very familiar in daily life, is News Item text. News Item texts can be found in newspapers, magazines, blogs, news websites, etc. Utilizing Google Translate to translate News Item texts is also popular due to the need of fast information updates in the society in a form of translating foreign news into local language that can be understood by local people and vice versa. Thus, this research was employed to find and explain errors in Google Translate’s translation results of News Item texts from Indonesia to English. RESEARCH METODOLOGY This study was a descriptive qualitative research, and it presented the data with a content analysis method. According to Moleong (2010: 6), qualitative study is a study which has the goal to understand the subject of the study in the descriptive way. According to Mujiyanto (2011: 23), qualitative approach tried to reveal the phenomenon comprehensively and appropriately with the context trough the natural data collection, employing the researcher as key instrument of the study. In line with Mujiyanto, according to Sutopo (2002:39) qualitative study employs the inductive analysis which is not meant to support or to reject the hypothesis, unlike in quantitative study. Moreover, according to Arikunto (2006: 343), drawing a conclusion in qualitative study can be done by comparing data with the certain given criteria such as equal, less equal and unequal. In addition, the data of qualitative study can also be analyzed trough simple statistical analysis in the form of ratio. Purposive sampling technique was used to select the sample of News Item texts. Purposive sampling techniques involve selecting certain unit or cases based on a specific purpose rather than randomly (Tashakkori & Teddlie, 2003: 713). 180-240 words was decided as the total words range for News Items texts used in this study. It was decided so that the sample texts would not be Adam Ismail / Journal of English Language Teaching 5 (2) (2016) 3 very short or very long to analyze. A total of five texts, each with 180-240 total words, were chosen among five Indonesian news websites. They were taken from (1) bbc.com, (2) kompas.com, (3) liputan6.com, (4) tempo.co, and (5) viva.co.id. The texts were labeled from Text A to Text E. The decision of taking five News Item texts as the sample size was taken because of the limited time and resources of the study. The five News Item texts were then translated into English by using Google Translate. I inputted the content of each text as a whole, instead of partially inputted the sentences one by one into Google Translate. It was done in order to make sure that Google Translate translated the content of the text cohesively and coherently. After Google Translate translation proccess was done, five Indonesian News Item texts and their English translations counterpart were obtained. The first step in data analysis was comparing the source and target texts to identify some translation errors based on the Indonesian- English translation error typology. Translation error typology from Dewi (2015) is chosen to be used in this study. Dewi’s translation error typology is used in this study because it is based on her adjustment of American Translation Associaton (ATA) framework for specific Indonesia-English language pair (2002). The errors include terminology, usage, syntax, mistranslation/misunderstanding, literalness/faithfulness, ambiguity, omission, addition, capitalization, punctuation, grammatical, and word form errors. Translation errors can occur in the words, phrases, clauses, or sentences of the target texts. To list the errors, translation errors tables were used. The format of the table can be seen below: Table 1. Format of Translation Errors Table No. Source Text Target Text Categories of Errors Comments 1. ... ... ... ... 2. ... ... ... ... ... ... ... ... ... The sentences from source and target texts were inputted into source text and target text columns. Words, phrases, clauses, and sentences that contained the errors were bold marked for the clear presentation of the errors occurrences. Each sentence from the source and target texts could be inputted more than once, because multiple errors could be occured in a single sentence. The researcher decided not to just input words/phrases/clauses into the tables because the errors could be understood clearer when they were written on their respective contexts. The categories of errors occurred were inputted on the categories of errors column. Brief explanations of the errors were inputted on the comments column. As there were five source texts and their target texts counterpart, five translation errors tables were made. The whole tables were used as the means to help in the data analysis and interpretation. FINDINGS AND DISCUSSION After going through data investigation process, 278 data containing translation errors based on the translation error typology were obtained from Text A, B, C, D, and E. The general findings are shown in the following table below. Adam Ismail / Journal of English Language Teaching 5 (2) (2016) 4 Table 2. General Findings No. Categories of Errors Frequency of Errors Total Errors (%) A B C D E 1. Terminology Errors 7 4 18 6 8 43 15.47% 2. Usage Errors 2 5 4 4 2 17 6.12% 3. Syntax Errors 8 5 12 8 1 34 12.23% 4. Mistranslation /Misunderstanding Errors 2 4 5 7 - 18 6.47% 5. Literalness /Faithfulness Errors 5 - 6 7 - 18 6.47% 6. Ambiguity Errors 3 1 2 - 1 7 2.52% 7. Omission Errors 12 7 10 5 5 39 14.03% 8. Addition Errors 2 - 4 2 - 8 2.88% 9. Capitalization Errors 1 1 2 1 1 6 2.16% 10. Punctuation Errors 4 1 - 4 1 10 3.60% 11. Spelling Errors - - 3 - - 3 1.08% 12. Grammatical Errors 22 5 17 15 9 68 24.46% 13. Word Form Errors 3 1 3 - - 7 2.52% The most notable errors from grammatical errors were singular-plural errors (35.29% of grammatical errors), incorrect tenses (35.29% of grammatical errors), and incorrect cases of pronoun (10.29% of grammatical errors). Terminology errors happened frequently for 43 times, spreading in every sample of News Item texts used in this study. The distribution of omission errors was spreaded almost evenly in the data. The distribution of syntax errors was skewed to unnatural word orders, which occured 26 times (76.47% of syntax errors). Mistranslation/ misunderstanding and literalness/faithfulness errors resulted in a tie, with each occured 18 times. Usage errors occured 17 times, spreading in every sample of News Item texts. The rest of the errors only occured less than 11 times, so they rarely occured in the data. The distribution of punctuation errors was spreaded almost evenly across the data. Addition errors occurred 8 times, but only occurred in three of five texts used in the study. Capitalization only occured in the context of title sentence writing and first letter capitalization in the data. Spelling errors were varied across the wrong spelling of number, proper noun, and date. However they only occured three times, and not spread evenly among the five samples of News Item texts. Talking about the subcategories of grammatical errors, singular-plural errors in the whole data occured because of Google Translate inability to identify the nouns in the source texts contexts, whether in the form of singular or plural. Google Translate also could not understand that the texts in this study were News Item texts, in which past tenses were used. Google Translate for several times used present tenses in the target texts, that should be corrected to past tenses in order to match the tense used in News Item texts. When Google Translate used articles in the target texts of the data, most of them were correct. On the other hand, in the data, Google Translate could not differentiate the word ‘namun’ which functioned as a connector to connect sentences, or as a conjunction to connect clauses in a sentence. Problems with subject-verb agreement in the data occured because Google Translate Adam Ismail / Journal of English Language Teaching 5 (2) (2016) 5 translated the plural nouns from the source texts into singular nouns in the target texts. The verbs following those incorrect translations would be incorrect as well, which resulted in grammatical errors. Since active-passive voice errors resulted in very different meaning of target texts compared to their source texts, this subcategory of grammatical error was important to consider. Google Translate also unable to identify some pronouns in accordance to the context of the target texts in the data, especially in identifying pronouns that were related to the previous sentences in the texts. Moreover, the whole terminology errors in the data were caused by word-to-word translations of phrases’ elements, and the inability of Google Translate in translating some words which have specific meanings out of the general equivalences of those words in the target texts to match the contexts. Omission errors found in the data were mostly caused by Google Translate, which for several times translate the source texts literally, ignoring the overall meaning of the target texts. The technical errors of Google Translate itself that omitted parts of the source texts were also the cause of these problems. Talking about the subcategories of syntax errors, the whole sentence fragments errors in the data were caused by the inability of Google Translate to identify and form complete sentence from the source texts. Even though the source texts had problems of sentence fragments, Google Translate could not correct them in the target texts just like what human who has sufficient knowledge of translating Indonesia to English would do. It was because Google Translate used statistical method in translating the source texts, which did not involve any linguistic rule at all. It would also made several usage errors when Google Translate encountered new cases of texts which it had not ever translated before. When encountering complex sentences in the data, Google Translate for several times failed to identify the phrases properly which resulted to improper modifications and bad translation results. Moreover, lack of linguistic rules employed in Google Translate also resulted in unnatural word orders in the target texts in the data. Mistranslation/misunderstanding errors in the data were caused by the inability of machine, in this case was Google Translate, to understand the contexts of source texts as a whole. Moreover, literalness/faithfulness errors made the arrangement of words in the translation results strange for the target language users. Usage errors were caused by the inability of Google Translate to form correct wordings in the target texts. Meanwhile, when punctuations in the source texts were correct, Google Translate did not make any punctuation error at all. In contrast, when the source texts had punctuation errors, Google Translate would also made punctuation errors in the target texts. In the data, Google Translate only added minor additions. Meanwhile, ambiguity errors found were mostly caused by redundancy and wrong choice of words. So, addition and terminology errors indirectly affected the occurences of these errors in the data. The researcher did not encounter any difficulty in identifying the word form of each word in the source texts. Therefore, it can be concluded that word form errors found in the data were caused purely by the incompetence of Google Translate in deciding the correct word form used in the target texts. There were only two basic cases of capitalization errors found in the data: in the title sentence writing of News Item texts, and in the first letter writing of sentences. Spelling errors in the data only occured in spelling of number, proper noun, and date. Since these errors only happened in 3 of 278 total data, and did not alter the meanings of the source texts, these translation errors can be conluded as not significant compared to the other translation errors mentioned before. Finally, there were 3 sub-categories of errors from the translation error typology that did not occur at all in the data. They were countable- uncountable errors (grammatical errors), Adam Ismail / Journal of English Language Teaching 5 (2) (2016) 6 incorrect conjunctions (grammatical errors), and lack of parallelism (syntax errors). CONCLUSIONS The findings of the analysis showed that there were 13 categories of translation errors obtained from 278 data. Ranking from the most common to the least common errors found in the data, they were grammatical errors (24.46%), terminology errors (15.47%), omission errors (14.03%), syntax errors (12.23%), mistranslation/misunderstanding errors (6.47%), literalness/faithfulness errors (6.47%), usage errors (6.12%), punctuation errors (3.60%), addition errors (2.88%), ambiguity errors (2.52%), word form errors (2.52%), capitalization errors (2.16%), and spelling errors (1.08%). The data findings also showed that statistical method of Google Translate, the inability of Google Translate to understand the contexts of the texts beyond sentence, and errors in the source texts that carried into translation errors in the target texts were the causes of translation errors found in the data. REFERENCES American Translator Association. 2002. Framework for Standard Error Marking, an Explanation. Web. January 21, 2016. Retrieved from http://www.atanet.org/bin/view.pl/12438.ht ml Arikunto, Suharsimi. 2006. 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