Error analysis of line translate`s performance in translating daily basic conversation in percakapan Bahasa Inggris sehari-hari - USD Repository
ERROR ANALYSIS OF LINE TRANSLATE ’S PERFORMANCE
IN TRANSLATING DAILY BASIC CONVERSATION IN
PERCAKAPAN BAHASA INGGRIS SEHARI-HARI
Presented as Partial Fulfillment of the Requirements for Degree of Sarjana Sastra in English Letters
By
M.Y. CHRISTY GHEDA PATI TIALA
Student Number: 144214045
DEPARTMENT OF ENGLISH LETTERS FACULTY OF LETTERS UNIVERSITAS SANATA DHARMA YOGYAKARTA 2018
ERROR ANALYSIS OF LINE TRANSLATE ’S PERFORMANCE
IN TRANSLATING DAILY BASIC CONVERSATION IN
PERCAKAPAN BAHASA INGGRIS SEHARI-HARI
Presented as Partial Fulfillment of the Requirements
for Degree of Sarjana Sastra
in English Letters
By
M.Y. CHRISTY GHEDA PATI TIALA
Student Number: 144214045
DEPARTMENT OF ENGLISH LETTERS
FACULTY OF LETTERS
UNIVERSITAS SANATA DHARMA
YOGYAKARTA
2018
Many plans are in a man ’s heart, but the purpose of the Lord will prevail.
(Proverbs 19:21) To Papa and Mama
ACKNOWLEDGEMENTS Firstly, I would like to express my gratitude to God for his blessing and
companion in every journey of my life, and finally I can write my
acknowledgements.Secondly, I want to thank my thesis advisor, Harris Hermansyah Setiajid,
M.Hum for helping me from the very beginning of this thesis writing. My thanks
go to my co-advisor, Anna Fitriati, S.Pd., M.Hum. for giving me valuable
suggestions to improve my thesis. Also, I would like to express my gratitude
towards all lecturers of English Letters Department for giving me inspiration
during my 4 years of study.I want to thank my father, Dami Tiala, for his hard work to support my
study. My gratitude towards my mother, Ritta Deske, who always has her faith
and understanding in every single path I took to finish my undergraduate thesis.In addition, I would like to mention some names who contributed during
this process of writing. I want to give my thanks to Lintang, Vincent, Titis,
Rangga, Philip, Flo, Karisa and Mira who helped me during my downs. I want to
thank Acit, Ayu, Festy, Shella, Didi and the others who belong to Kelas B kenyil
group who supported me to finish my thesis. Also, I want to thank Wira and Arsa.
Thank you for the every laughter, lesson and moment we shared.Last but not least, I want to thank everyone that cannot be mentioned here
who helped me in their capacity to finish my undergraduate thesis, God bless you.
M.Y. Christy Gheda Pati Tiala
TABLE OF CONTENTS
TITLE PAGE............................................................................................... ii
APPROVAL PAGE..................................................................................... iii
ACCEPTANCE PAGE............................................................................... iv
STATEMENT OF ORIGINALITY........................................................... v
PUBLIKASI KARYA ILMIAH................................................................. vi
MOTTO PAGE............................................................................................ vii
DEDICATION PAGE................................................................................. viii
ACKNOWLEDGEMENTS........................................................................ ix
TABLE OF CONTENTS............................................................................ x
LIST OF ABBREVIATIONS..................................................................... xii
LIST OF TABLES....................................................................................... xiii
ABSTRACT.................................................................................................. xiv
ABSTRAK.................................................................................................... xv
CHAPTER I: INTRODUCTION............................................................... 1
A. Background of Study.................................................................... 1 B. Problem of Formulation................................................................ 3 C. Objectives of the Study................................................................. 4 D. Definitions of Terms..................................................................... 4CHAPTER II: REVIEW OF LITERATURE.......................................... 5
A. Review of Related Studies............................................................ 51. Kurnianto ’s thesis...................................................................... 5
2. Ariany ’s thesis.......................................................................... 6
3. Veronika ’s thesis....................................................................... 7 B. Review of Related Theories.......................................................... 8
1. Translation................................................................................. 8
2. Machine Translation.................................................................. 9
3. Koponen ’s Error Classification in Assessing Machine Translation Quality................................................................... 10
C. Theoretical Framework................................................................. 13
CHAPTER III: METHODOLOGY........................................................... 14
A. Areas of Research......................................................................... 14 B. Object of the Study........................................................................ 15 C. Method of the Study...................................................................... 15 D. Research Procedure....................................................................... 161. Types of Data............................................................................ 16
2. Data Collection.......................................................................... 17
3. Population and Sample.............................................................. 18
4. Data Analysis........................................................................... 18
CHAPTER IV: ANALYSIS RESULT AND DISCUSSION.................... 20
A. The Errors Found in the Translation of Indonesian Daily Conversation.................................................................................. 201. Omitted Concept........................................................................21
2. Added Concept.......................................................................... 23
3. Untranslated Concept................................................................ 26
4. Mistranslated Concept............................................................... 29
5. Substituted Concept................................................................... 37
B. Line Translate performance in Translating Expression in
Particular Chapters........................................................................ 391. The Performance of Line Translate in Translating Greetings... 40
2. The Performance of Line Translate in Translating Thanks...... 42
3. The Performance of Line Translate in Translating Parting....... 45
4. The Performance of Line Translate in Translating Excuses and Apologies........................................................................... 47
CHAPTER V: CONCLUSION................................................................... 50
BIBLIOGRAPHY........................................................................................ 52APPENDICES.............................................................................................. 54
Appendix 1........................................................................................ 54 Appendix 2........................................................................................ 78LIST OF ABBREVIATIONS
E : Excuses and Apologies G : Greetings MT : Machine Translation OA : Official Account P : Parting SL : Source Language ST : Source Text T : Thanks TL : Target Language TT : Target Text
LIST OF TABLES No. Table Page
5. Table 5. Untranslated Concept
8. Table 8. Total Errors in Each Chapter
37
7. Table 7. Substituted Concept
29
6. Table 6. Mistranslated Concept
26
24
1. Table 1. Example Data Coding
4. Table 4. Added Concept
24
3. Table 3. Omitted Concept
19
2. Table 2. Example of Data Classification
17
40
ABSTRACT
TIALA, M.Y. CHRISTY GHEDA PATI. (2018). Error Analysis of Line
Translate ’s Performance in Translating Daily Basic Conversation inPercakapan Bahasa Inggris Sehari-hari. Yogyakarta: Department of English
Letters, Faculty of Letters, Sanata Dharma University.Machine Translation (MT) is one of the proofs for the rising of
technology. MT helps humans to translate a language to another language. One of
the advantages for using MT is that time is faster. However, the result of the
translation is not perfect. The translation of MT still need to be post-edited. In
2015, Line as one of the social media established a MT feature to translate, ID-EN
Translator.ID-EN Translator can translate Indonesian to English and vice versa. As already stated that the product of MT ’s translation is not perfect, the researcher
is interested to study the error ID-EN Translator might make. In addition, the
research chooses a book entitled Percakapan Bahasa Inggris Sehari-hari as the
data for this study.In this study, the researcher analyzes two problems. The first is to find and
classify the errors in the translation of Indonesian daily conversation by Line
Translate . The second one is to see Line Translate performance in translating
expression in particular chapters.In this research, the researcher uses library method in order to study the
theories applied and the related studies. Furthermore, explicatory method is also
used in order to analyze the data.In the final result, the researcher finds that Line Translate makes 59 errors:
6 errors in omitted concept, 5 errors in added concept, 11 errors in untranslated
concept, 29 errors in mistranslated concept, and 8 errors in substituted concept.
Meanwhile, the most errors happen in Thanks (31,51), the second in Excuses and
Apologies (28,81%), the third in Greetings (23,73%), and the last one in Parting
(16,95%). It can be seen that Line Translate makes the best performance in
translating Parting and the worst performance in Thanks.
ABSTRAK
TIALA, M.Y. CHRISTY GHEDA PATI. (2018). Error Analysis of Line
Translate ’s Performance in Translating Daily Basic Conversation inPercakapan Bahasa Inggris Sehari-hari. Yogyakarta: Program Studi Sastra
Inggris, Fakultas Sastra, Universitas Sanata Dharma.Mesin penerjemah merupakan salah satu hasil dari perkembangan
teknologi. Mesin penerjemah membantu manusia untuk menerjemahkan suatu
bahasa ke bahasa lain. Salah satu keuntungan dalam penggunaan mesin
penerjemah adalah waktu yang singkat. Walau demikian, terjemahan dari mesin
penerjemah dinyatakan tidak sempurna, perlu adanya pengoreksian ulang. Pada
tahun 2015, Line sebagai salah satu media sosial meluncurkan ID-EN Translator
sebagai salah satu fitur mesin penerjemah untuk menerjemahkan. ID-EN
Translator dapat menerjemahkan Bahasa Indonesia ke Bahasa Inggris dan
sebaliknya. Namun, seperti disebutkan di atas bahwa hasil terjemahan mesin
penerjemah tidak sempurna, maka peneliti tertarik untuk mencari kesalahan yang
dibuat ID-EN Translator. Peneliti memilih buku berjudul Percakapan Bahasa
Inggris Sehari-hari sebagai data untuk penelitian ini.Di dalam studi ini, penulis menganalisis dua masalah. Yang pertama
adalah menemukan kesalahan di dalam hasil terjemahan Line Translate dan yang
kedua adalah melihat kinerja Line Translate dalam menerjemahkan ekspresi
dalam setiap bab yang telah dipilih dalam penelitian ini.Peneliti menggunakan metode pustaka dalam penulisan teori dan
penelitian yang berkaitan dengan penelitian ini. Selanjutnya, peneliti juga
menggunakan metode explicatory untuk menganalisa data.Hasil penelitian ini menunjukan bahwa Line Translate membuat sebanyak
59 kesalahan. 6 kesalahan pada konsep omitted, 5 kesalahan pada konsep added,
11 kesalahan pada konsep untranslated, 29 kesalahan pada konsep mistranslated
dan 8 kesalahan pada konsep substituted. Sementara itu kesalahan paling banyak
terjadi pada bab Thanks (31,51%). Yang kedua adalah bab Excuses and Apologies
(28,81%). Yang ketiga adalah bab Greetings (23,73%) dan yang terakhir adalah
bab Parting (16,95%). Secara umum dapat disimpulkan bahwa kinerja Line
Translate paling baik saat menerjemahkan bab Parting dan paling buruk saat
menerjemahkan bab Thanks.CHAPTER I INTRODUCTION A. Background of The Study Language and human beings cannot be separated. Pei states that “language
is an expression of huma n activity” (1984:26). It means people always use
language to prove their existence. As people live in different countries, people
also speak in different languages. When people communicate in different
languages, they cannot understand each other. Therefore, people translate to
produce translation to overcome this situation.According to Nida and Taber , “translating consists of reproducing in the
receptor language the closest natural equivalence of the source language message,
first in terms of meaning and secondly in terms of style” (1982:12). It means that
translating is a process which changes the Source Language (SL) into the Target
Language (TL). The process must change the language into another language
which the Target Text TT) has the same meaning and style from the Source Text
(ST).In this modern era, technology is one of the needs of human beings. Every
aspect of human beings can be related to technology. One of the results of
technology is social media. Social media help people to communicate through the
internet. There are various forms of social media equipped with their own
characteristics. The examples of social media are Instagram, Twitter, Facebook
and also Line. Each social media provides their own characteristics to help the
users communicate easily. Line as of the social media is chosen for this study.“Line was established in 2011 in Japan and 2013 in Indonesia and until now it has more than 169 million users ” (LINE - Statistics & Facts). The users are
widely spread around the world. Line provides the users with various features,
such as chat room to have a written conversation with other users, home to post
update news, Line free call to make a phone call with another user and also Line
Translate . Line Translate was established in 2015. The form of Line Translate is
an Official Account (OA). OA is a public account that can be accessed by all Line
users. OA is provided by Line itself or a corporation/brand that agrees to
cooperate with Line. For the users who want to make the use of it, they need to
add the OA of Line Translate into their friends‟ list. After that, they can translatethe text in the chat room. This study chooses ID-EN Translator as the subject of
the study. ID-EN Translator is an OA to translate Indonesian to English and vice
versa. In addition, from now on, ID-EN Translator will be stated as Line
Translate in this study.This undergraduate thesis discusses the performance of ID-EN Translator
or Line Translate as a Machine Translation (MT). The researcher studies the
performance of Line Translate in translating Indonesian daily basic conversation.
There are two reasons for choosing daily basic conversation as the subject. First,
daily basic conversation is important considering that it is used in everyday‟s life.Second, it consists of simple phrases and sentences that commonly used. In
addition, Line is commonly used for having a daily conversation.One of the ST is ada baik jualah, terima kasih which is translated by Line Translate into „there are both sell, thank you.‟ From the translation, it is clearly
seen that the TT has different meaning from the ST. In this case, the word jualah
comes from the word juga and particle -lah which means „also‟ or „too.‟ However, Line Translate understands it as jual-lah which means „to sell.‟ Therefore, the translation becomes „sell.‟ The translation should be „quite well, thank you.‟ From this case, it shows that the message transferred is different.
Therefore, the researcher is interested to study the performance of Line Translate
by studying errors found in the translation of Indonesian daily basic conversation.
This research is expected to help the readers, especially students in Sanata
Dharma University to see the performance of Line Translate as a MT in
translating daily basic conversation from Indonesian to English. In addition, this
study hopefully will be one of the consideration to improve the performance of
Line Translate .B. Problem Formulation
There are two research questions in this undergraduate thesis which would be analyzed. They are formulated into two questions as follow:
1. What errors are found in the English translation of Indonesian daily
conversation done by Line Translate based on Koponen‟s category?
2. How does Line Translate perform in translating expression in particular
chapters of Indonesian daily conversation?C. Objectives of The Study There are two objectives of this study. First is to find out the errors in the translation of Indonesian daily conversation using
Koponen’s Assessing Machine
Translation Quality with Error Analysis and second is to analyze the performance
of Line Translate in translating the expression in particular chapters of Indonesian
daily basic conversation.D. Definitions of Terms Line is a Japan-based, cross-platform mobile messenger app with
The service is operated by Line
Corporation (LINE - Statistics & Facts).Machine Translation is the now traditional and standard name for
computerized system responsible for the production of translations from one
natural language into another, with or without human assistance (Hutchins and
Somers, 1992:3)Error Analysis in Machine Translation is the analysis with a view to
identify different error types which focuses on mismatches of semantic
components in machine translation (Koponen, 2010:9)CHAPTER II REVIEW OF LITERATURE In this chapter, the researcher discusses similar topics done by other
researchers and also the theories which are applied in this study. The related
studies are taken from Kurnianto's, Ariany's and Veronika's thesis. Each study is
discussed and elaborated to see the main focus and the similarities to this research.
In addition, to avoid the same topic, the researcher also reviews the study's
distinction between the related studies and the present undergraduate thesis. The
theories are also reviewed and discussed in order to understand this research.A. Review of Related Studies
1. Kurnianto's thesis "Google Translate Assesment with Error Analysis: An
Attempt to Reduce Error" This undergraduate thesis done by Kurni anto discusses Google Translate’sassessment of error analysis and the attempts to reduce the errors. The writer uses
three source texts. They are the ownership agreement document, a national
geography's article, and iPad user guide.This study has two research question. The first one is what errors found in
the source text translated by Google Translate and the second is what suggestions
proposed to reduce errors in using Google Translate. In order to answer the
research questions, the writer uses Koponen's theory to assess machine translation.
In addition, the writer uses Farrus' solution in optimizing the use of machine
translation.This study shows that Google Translate makes 206 errors. The writer also
discovers that the most errors are mistranslated concepts which are 136 errors.
Kurnianto also suggests Google Translate to reduce errors, firstly translated in
isolated form covering type in lower case, functions categorization, search the
appropriate equivalence form the list of meaning, and type the phrase without
"enter", secondly text edition, and the combination of some methods.The research done by Kurnianto discusses Google Translate while this present undergraduate thesis chooses Line Translate as its subject of the study.
2. Ariany's thesis "Bing Translator's and Google Translate's Performance in
Translating English Literary and Academic Text into Indonesian" In this undergraduate thesis, Ariany discusses Bing Translator and GoogleTranslate performance. There are two source texts chosen, english literary and
academic text. In this study, Ariany studies two objectives, the first is to find out
the errors in the translation of literary and academic text using Bing Translator
and Google Translate, and the second is to measure Google Translate and Bing
Translator performance in translating the literary and academic texts. Ariany uses
Koponen's theory to conduct this research.This study shows that Google Translate makes fewer errors than Bing
Translator. It also shows that Google Translate has better performance than Bing
Translator.
3. Veronika's thesis "Instagram Translate's and Human Translation's
Performance in Translating the Captions in @Basukibtp Instagram Account"This undergraduate thesis discusses the comparison between Instagram
Translate and human translator. The object of the research is Basuki Tjahja
Purnama's instagram account.This research focuses on two objectives. The first one is to find out the
errors of English translation done by Instagram Translate and human translator
and the second is to compare the performance of Instagram Translate and human
translator. The researcher uses Koponen's theory which divides the errors into six
subclasses to find and classify the errors.This study shows that in the final result there are 54 errors done by
Instagram Translate meanwhile human translator makes 6 errors. After having the
results, it can be said that the performance of human translator is better than
Instagram Translate.This present undergraduate thesis is different from Veronika's on its
subject. Veronika puts the comparison between Instagram Translate and human
translator as the focus of this research while the present researcher studies on Line
Translate 's performance in translating Indonesian daily basic conversation.B. Review of Related Theories
1. Theories of Translation
Bell states, "translation is the expression in another language (or target
language) of what has been expressed in another, source language, preserving
semantic and stylistic equivalence" (1991:5). It means translation is a replacement
of language to another which tries to maintain the same impression from the ST
and TT. Semantic equivalence focuses on sending the same message and stylistic
equivalence focuses on sending the same sense between ST and TT.For example is a proverb bagai air di atas daun alas, it can be translated
into both ‘like water on taro leaves’ or ‘people who don’t have principle.’ The
first translation tries to maintain the style of ST, meanwhile the second translation
translate the meaning directly. Therefore, the first one is considered into stylistic
and the second is semantic equivalence.He also adds that, "translation is the replacement of a representation of a
text in one language by a representation of an equivalent text in a second
language" (1991:6). Here, Bell emphasizes again the importance of presenting the
same idea in the TT.This statement is also supported by Nida and Taber. They state that,
"translation consists in reproducing in the receptor language the closest natural
equivalent of the source language, first in terms of meaning and secondly in terms
of style” (1974:12). In addition, Nida and Taber also state that transferring
meaning is a priority in terms of translation (1974:13). Here, translation puts the
focus on giving the same meaning and style in the TT from the ST. The
translation is expected to maintain or keep the style of the text without changing
the meaning.From those experts, it can be concluded that translation is a process of
changing a language (SL) into another language (TL) that gives the same sense
considering the meaning and style.2. Machine Translation
According to Hutchin and Somers, "The term Machine Translation (MT)
is the now traditional and standard name for computerized system responsible for
the production of translations from one natural language into another, with or
without human assistance" (1992:3). It means that the term MT is related to
artificial intelligence that helps human to produce a translation.In MT characteristics, Hutchin and Somers also state, Translations produced by MT systems are inadequate. The MT systems cannot make good translation result which is equivalent in terms of semantic and stylistic. He adds that machine translation is not possible to produce a good translation. Machine translation merely stops in a phase of ‘raw translation' which still need revising or post- editing (Hutchin and Somers 1992:3). It means that the translation produced by MT should be examined or post-
edited. It happens because MT is only an artificial intelligence which its capability
has limitation. It is also uncertain that MT can produce a perfect translation
considering the meaning and style. Therefore, if MT is assumed cannot make a
perfect translation, it can be studied further to the errors MT makes.In addition, Koponen states that "Machine translation assessment has
mainly been microtextual and focused on the aspect of accuracy and fluency"
(2010:1). It means the assessment focuses on how MT can accurately and fluent
transfer the message. She also adds "Quality assessment involves various aspect,
such as accuracy (fidelity), fluency and fitness for purpose, and different aspects
have been deemed important for every situation" (2010:1).From those experts, it can be said that MT is a system to translate
language from ST into TT which still need to be post-edited. MT is considered not
able yet to produce a good translation considering its meaning and style.
3. Koponen's Error Classification in Assessing Machine Translation Quality
As stated above that MT is still considered not able to produce a perfecttranslation, but it can be traced to what errors might MT make. Therefore,
Koponen in her journal, Assessing Machine Translation Quality with Error
Analysis (2010), proposes the idea to assess the quality of MT's work using errors
analysis.Koponen divides the errors into two big classes, first is mismatches
between source and target concepts and second is mismatches in relation between
concepts. The concept meant by Koponen is the idea presented in the ST and TT.
Error in the first category is represented by the content words while the error in
the second category represented by function words, inflection, and word order.Error between source and target text concepts is divided into six
subclasses. They are omitted concept, added concept, untranslated concept,
mistranslated concept, substituted concept, and explicitated concept. The second
class is errors on relation between concepts. The second class is divided into eight
subclasses, they are omitted participant; and relation, added participant; and
relation, untranslated participant; and relation, mistranslated participant; and
relation. The meaning of each error is provided by Koponen while the elaboration
and examples are provided by the researcher and Koponen.a. Mismatches between source and target concepts (Koponen, 2010: 4-5) i. Omitted concept happens when ST concept is not conveyed by the TT.
It means the concept that appears in the ST is not presented in the TT. For example, when the ST is Anda ada pensil lainnya? the TT is ‘You've got a pencil?’ The concept of lainnya is omitted in the TT.
ii. Added concept happens when TT concept that is not present in the ST.
It means that there is an additional concept in the TT which previously is not present in the ST. For example, Saya akan telepon lagi nanti is translated into ‘I'll call you later.’ It can be seen that ‘you’ which appears in TT does not exist in ST before. iii. Untranslated concept occurs when SL words that appear in TT. It means that the word in ST is not possible to be translated, therefore the word appears again in the TT. For example, Tidak apa-apalah in the ST is translated into
‘No apa-apalah.’ The word apa-apalah is considered into untranslated concept because they appear in both ST and TT. iv. Mistranslated concept exists when a TT concept has the wrong meaning for the context. It means that the TT has the wrong meaning. It can happen because a word can have more than one meaning. For example, Nyonya bagaimana kabarnya? in ST is translated into
‘gentlemen how are you? ’ Nyonya means a woman who is married already, but it is translated into ‘gentleman’ which is an address to a man. v. Substituted concept happens when TT is not a direct lexical equivalent for ST concept but it can be considered as a valid replacement for the context. For example, O, John berkata minta sampaikan salamnya pada anda in ST is translated into
‘Oh, John said to say hello to you.’ In this case, salamnya is not considered equivalence for ‘hello.’ However, in this context
‘hello’ is considered as a valid replacement of salam in the ST. vi. Explicitated concept occurs when TT concept explicitly states information left implicit without adding information. It means there is an additional concept in TT which is not explicitly stated in ST. For example on Koponen's research, there is an addition of the word ohjelma which means ‘a program.’ Ohjelma is added to ‘Norton Antvirus
’.
b. Mismatches in relations between concepts
i. Omitted participant: ST relation not conveyed by the TT due to an omitted head or dependant ii. Omitted relation: ST relation not conveyed by the TT due to morphosyntactic errors that prevent parsing the relation although both concepts are present in the TT.
iii. Added participant: TT relation not present in ST introducing an added concept.
iv. Added relation: TT relation not present in ST arises due to morpho- syntactic errors. v. Mistaken participant: Head or dependant of the relation different in ST and TT, not the same entity. vi. Mistaken relation: Relation between two concepts different in ST and TT, changed role. vii.Substituted participant: Head or dependant of the relation different in ST and TT, same entity. viii.Substituted relation:Relation between two concepts different in ST and TT, same semantic roles.
C. Theoretical Framework
The theories of translation according to Bell and Nida and Taber are used
to give understanding about the meaning of translation. Then, the theory of MT by
Hutchin and Somers is used to study Line Translate as the subject of this study.
After knowing the definitions, Koponen’s theory of Assessing MachineTranslation Quality with Error Analysis is used to identify the errors done by Line
Translate . By identifying the errors, the first research question in this study is
answered. Then, the error categorization is used to answer the second research
question, Line Translate’s performance in translating expression in particular
chapters. By answering the first and second research question, the researcher is
able to determine Line Translate ’s performance in translating Indonesian daily basic conversation.CHAPTER III METHODOLOGY This chapter presents the methodology used in this undergraduate thesis. There are four parts discussed in this chapter. They are area of research, object of
the study, method of the study, and research procedure. The last part, research
procedure discusses types of the data, data collection, population and sample, and
data analysis.A. Areas of Research
The area of this research is translation and technology. According to
William and Chesterman, “there are three range topics to be discussed in
translation and technology, they are evaluating software, software localization,
and effects of technology” (2002:14). This research takes ID-EN Translator or
stated as Line Translate as its subject of the study. It focuses on the translation of
Indonesian daily basic conversation done by Line Translate. In addition, it also
evaluates Line Translate’s performance in translating expression in particular chapters.
William and Chesterman also add that , “in evaluating software, language
engineering is producing more and more software for machine translation and
computer- aided translation” (2002:14). Therefore, this study is included intotranslation and technology on evaluating software because this research studies
and evaluates the performance of Line Translate as a machine translation.The researcher's goal is to find out the mismatches or errors in translating
daily basic conversation. After finding the errors, the researcher is able to measure
the performance of Line Translate.B. Object of the Study The object of this study is the English translation of Indonesian daily
conversation. The data are taken from a book entitled Percakapan Bahasa Inggris
Sehari-hari by S.F.Habeyb. It was published by PT Bhuana Ilmu Populer,
Kelompok Gramedia, Jakarta. The data population consists of 22 chapters of
expression. However, the researcher only takes four chapters as the data sample.
They are Greeting, Parting, Thanks, and Excuses and Apologies. There are two
reasons for choosing only 4 chapters out of 22. First, the 4 chapters are considered
the most important expression and the second, the 4 chapters consists of
expression that is commonly used in every day’s life.The formation of the data is a text conversation between 2 people. The
data then are translated by Line Translate. The translations are divided into
sentences and classified into the error categories they belong to.C. Method of The Study In this study, the researcher uses library research method in writing the
chapter of literature and theories review. According to George, "library method
involves identifying and locating sources that provide factual information or
personal/ expert opinion on a research question; necessary component of every
other research method at some point" (2008:6). Library method is used to study
related theories and study.In addition, the researcher also uses explicatory method. George states,
"explicatory method entails a careful, close and focused examination of a single
major text, or of evidence surrounding a single complex event, in an attempt to
understand one or more aspects of it" (2008:6). Explicatory method is used to
identify and also analyze the errors in the English translation of Indonesian daily
basic conversation closely and carefully.D. Research Procedure
1. Types of Data
The data collected in this undergraduate thesis are taken from the ST and
the TT. The ST is taken from the conversation in a book entitled Percakapan
Bahasa Inggris Sehari-hari by S.F.Habeyb published by PT Bhuana Ilmu
Populer, Kelompok Gramedia, Jakarta. The TT is taken from the translation done
by Line Translate. The book has been printed for 30 times, and the last one was
on September 2016.There are four chapters taken as the data. They are Greetings, Thanks,
Parting, and Excuses and Apologies. There are 16 conversations and 71