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 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. Prosedur Penelitian, Suatu 

Pendekatan Praktik (Edisi Revisi VI). Jakarta: 

PT. Rineka Cipta 

 

Catford, J.C. 1965. A Linguistic Theory of 

Translation: An Essay in Applied Linguistics. 

London: Oxford University Press. 

 

Dewi, H. D. 2015. Comparing Two Translation 

Assessment Models: Correlating Student 

Revisions and Perspectives. Ohio: Kent State 

University. 

 

Moleong, L. J. 2010. Metode Penelitian Kualitatif. 

Bandung: PT. Remaja Rosdakarya. 

 

Mujianto, Yan. 2011. Petunjuk Penulisan Skripsi. 

Semarang: UNNES Press. 

 

Sutopo, H. B. 2002. Metodologi Penelitian Kualitatif 

Dasar Teori dan Terapannya dalam Penelitian. 

Surakarta: Sebelas Maret University. 

 

Tashakkori, A., & Teddlie, C. 2003. Handbook of 

Mixed Methods in Social & Behavioral 

Research. Thousand Oaks, CA: Sage. 

 


