-
INSTAGRAM TRANSLATE’S AND HUMAN
TRANSLATION’S PERFORMANCE IN TRANSLATING THE
CAPTIONS IN @BASUKIBTP INSTAGRAM ACCOUNT
AN UNDERGRADUATE THESIS
Presented as Partial Fulfillment of the Requirements
for the Degree of Sarjana Sastra
in English Letters
By
STEFANI VERONIKA
Student Number: 134214137
ENGLISH LETTERS STUDY PROGRAM
DEPARTMENT OF ENGLISH LETTERS
FACULTY OF LETTERS
SANATA DHARMA UNIVERSITY
YOGYAKARTA
2018
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INSTAGRAM TRANSLATE’S AND HUMAN
TRANSLATION’S PERFORMANCE IN TRANSLATING THE
CAPTIONS IN @BASUKIBTP INSTAGRAM ACCOUNT
AN UNDERGRADUATE THESIS
Presented as Partial Fulfillment of the Requirements
for the Degree of Sarjana Sastra
in English Letters
By
STEFANI VERONIKA
Student Number: 134214137
ENGLISH LETTERS STUDY PROGRAM
DEPARTMENT OF ENGLISH LETTERS
FACULTY OF LETTERS
SANATA DHARMA UNIVERSITY
YOGYAKARTA
2018
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INSTAGRAM TRANSLATE'S AND HUMANTRANSLATION'S PERFORMANCE IN
TRANSLATING THE
CAPTIONS IN @BASlJKIBTP INSTAGRAM ACCOUNT
. Harris Hennansyah Setiajid, M.Hum.Advisor
Anna Isti'anah, S.Pd.. M.Hum.Co-Advisor
III
November 30, 2017
December 15,2017
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LEMBAR PERNYATAAN PERSETUJUAN PUBLIKASI KARYA ILMIAH
UNTUK KEPENTINGAN AKADEMIS
Yang bertanda tangan di bawah ini, saya mahasiswa Universitas
Sanata Dharma
Nama : Stefani Veronika
Nomor Mahasiswa : 134214137
Demi pengembangan ilmu pengetahuan, saya memberikan kepada
Perpustakaan
Universitas Sanata Dharma karya ilmiah saya yang berjudul
INSTAGRAM TRANSLATE’S AND HUMAN
TRANSLATION’S PERFORMANCE IN TRANSLATING THE
CAPTIONS IN @BASUKIBTP INSTAGRAM ACCOUNT
Beserta perangkat yang diperlukan (bila ada). Dengan demikian
saya memberikan
kepada Perpustakaan Universitas Sanata Dharma hak untuk
menyimpan,
mengalihkan dalam bentuk media lain, mengelolanya dalam bentuk
pangkalan
data, mendistribusikan secara terbatas, dan mempublikasikannya
di internet atau
media lain untuk kepentingan akademis tanpa perlu meminta ijin
kepada saya
maupun memberikan royalti kepada saya selama tetap mencantumkan
nama saya
sebagai penulis.
Demikian pernyataan ini saya buat dengan sebenarnya.
Dibuat di Yogyakarta
Pada tanggal 29 November 2017
Yang menyatakan,
Stefani Veronika
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STATEMENT OF ORIGINALITY
I certify that this undergraduate thesis contains no material
which has been
previously submitted for the award of any other degree at any
university, and that,
to the best of my knowledge, this undergraduate thesis contains
no material
previously written by any other person except where due
reference is made in the
text of the undergraduate thesis.
Yogyakarta, November 29, 2017
Stefani Veronika
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STAY FOOLISH.
-Steve Paul Jobs-
STAY HUNGRY.
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TO YOU, MA&PA
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ACKNOWLEDGEMENTS
First thing first, I would like to send my sincere gratitude to
my best
friend, Jesus Christ for His blessing so that now I can write
down my
acknowledgements joyfully.
Needless to say, for sure I always cannot list everyone’s names
who
helped me not only finish my thesis but also give me such a
meaningful
experience during my study in Universitas Sanata Dharma. Some
names,
however, are mentioned in this page, just because I really
cannot express how
thankful I am to be supported directly by them.
Obviously, to my mother Tan Li Li and my father Shi Siang Siu.
Thank
you for everything, everything and everything. My cool thesis
advisor, Harris
Hermansyah Setiajid, M.Hum for always helping me from the very
beginning
of this research conducted every time, like every time I needed
help. Special
thanks goes to my thesis co-advisor, Arina Isti’anah, S.Pd.,
M.Hum for
providing suggestions and insight to improve my thesis.
My Cendani III troops, Sesilia Gisela Serat, S.S and Novita
Sari, S.S, who
always stand by my side, 24/7. For my friend, Theresia Esti M.
and my always-
be-there-friends, Mareta Anggita, B.IHM (hons) and Hendrike F.
Cherry
Sumarauw, S.T. I also would like to send my huge thanks to my
friends in Public
Relations of USD, JLTC, and also Kelas D Masih Skripsi’s
Group.
Stefani Veronika
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TABLE OF CONTENTS
TITLE PAGE
....................................................................................................ii
APPROVAL PAGE
..........................................................................................iii
ACCEPTANCE PAGE
....................................................................................iv
LEMBAR PERNYATAAN
PERSETUJUAN PUBLIKASI KARYA ILMIAH
............................................v
STATEMENT OF ORIGINALITY
................................................................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 the Study
....................................................................1
B. Problem Formulation
..........................................................................4
C. Objectives of the Study
......................................................................4
D. Definition of Terms
............................................................................4
CHAPTER II: REVIEW OF LITERATURE
................................................6
A. Review of Related Studies
..................................................................6
B. Review of Related Theories
...............................................................10
C. Theoretical
Framework.......................................................................16
CHAPTER III: METHODOLOGY
................................................................17
A. Area of Research
...............................................................................17
B. Object of the Study
.............................................................................18
C. Method of Study
.................................................................................18
D. Research Procedure
............................................................................19
1. Types of Data
................................................................................19
2. Data Collection
..............................................................................19
3. Population and Sample
..................................................................21
4. Data Analysis
.................................................................................22
CHAPTER IV: ANALYSIS RESULTS AND DISSCUSSION
.....................25
A. The Errors Found in @basukibtp Instagram Captions
......................25
1. The Translation Errors by Instagram Translate Found in
@basukibtp Instagram Captions
..................................................26
a. Omitted Concept
.................................................................26
b. Added Concept
....................................................................29
c. Untranslated
Concept...........................................................32
d. Mistranslated Concept
.........................................................34
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e. Substituted Concept
.............................................................38
2. The Errors Found in @basukibtp Instagram Captions by
Human
Translator
..................................................................................41
a. Omitted Concept
................................................................41
b. Added Concept
...................................................................43
c. Mistranslated Concept
........................................................44
d. Substituted Concept
...........................................................46
e. Explicitated Concept
.........................................................47
B. The Performance of Instagram Translate and Human Translator
in
Translating @basukibtp Captions
.....................................................48
1. The Performance of Instagram Translate and Human
Translator
in Omitted
Concept......................................................................50
2. The Performance of Instagram Translate and Human
Translator
in Added Concept
........................................................................51
3. The Performance of Instagram Translate and Human
Translator
in Untranslated Concept
..............................................................52
4. The Performance of Instagram Translate and Human
Translator
in Mistranslated Concept
.............................................................54
5. The Performance of Instagram Translate and Human
Translator
in Substituted Concept
.................................................................55
6. The Performance of Instagram Translate and Human
Translator
in Explicitated Concept
...............................................................57
CHAPTER V: CONCLUSION
........................................................................59
BIBLIOGRAPHY
.............................................................................................62
APPENDICES
...................................................................................................64
Appendix 1: All Data Translated by IgT
................................................64
Appendix 2: All Data Translated by HT
................................................67
Appendix 3: Description of Errors
.........................................................71
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LIST OF ABBREVIATIONS
ST Source Text
SL Source Language
TT Target Text
TL Target Language
MT Machine Translate
IgT Instagram Translate
HT Human Translator
No. Number of datum
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LIST OF TABLES
No. Table page
1. Table 1 Example of the Data Coding 21
2. Table 2 Description of error in Instagram Translate 23
3. Table 3 Description of error in Human Translator 24
4. Table 4 Omitted Concept by Instagram Translate 26
5. Table 5 Added Concept by Instagram Translate 30
6. Table 6 Untranslated Concept by Instagram Translate 33
7. Table 7 Mistranslated Concept by Instagram Translate 35
8. Table 8 Substituted Concept by Instagram Translate 39
9. Table 9 Omitted Concept by Human Translator 41
10. Table 10 Added Concept by Human Translator 43
11. Table 11 Mistranslated Concept by Human Translator 44
12. Table 12 Substituted Concept of Human Translator 46
13. Table 13 Explicitated Concept by Human Translator 47
14. Table 14 Total Errors by IgT and HT 49
15. Table 15 Total Errors in Omitted Concept done by IgT and HT
50
16. Table 16 Total Errors in Added Concept done by IgT and HT
51
17. Table 17 Total Errors in Untranslated Concept done by IgT
and HT 53
18. Table 18 Total Errors in Mistranslated Concept done by IgT
and HT 54
19. Table 19 Total Errors in Substituted Concept done by IgT and
HT 55
20. Table 20 Total Errors in Explicitated Concept done by IgT
and HT 57
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ABSTRACT
VERONIKA, STEFANI. Instagram Translate’s and Human
Translation’s
Performance in Translating the Captions in @Basukibtp Instagram
Account.
Yogyakarta: Department of English Letters, Faculty of Letters,
Sanata Dharma
University, 2017.
The paramount aspect of translating is when one meaning in
source
language is well-conveyed in the target language. Translating
can be done not
only by a professional translator but also machines translate.
In 2016, a social
media called Instagram started providing a translation feature
for its users to
translate captions. However, the researcher finds errors related
to the meaning
conveyed from source texts to target texts, Indonesian to
English. The errors are
found in Instagram accounts which owned by an Indonesia
politician, Basuki
Tjahaja Purnama or commonly known as Ahok. Based on that
phenomenon, the
researcher would like to investigate further about translation
result done by
Instagram Translate and compare it to a human translator.
In this research, there are two problems to solve. The first one
is to find
error exists in the result of translation done by Instagram
Translate and human
translator. The second one is to find out the different
performances of each
translation which might be reflected through the errors
made.
In order to deal with the problems, this research applied
library and
explicatory methods. Furthermore, the qualitative research
method was also
applied to demonstrate the findings.
In the final result, the researcher finds the total error found
in Instagram
Translate is 54, which 15 errors in Omitted Concept, 6 errors in
Added, 4 errors in
Untranslated, 24 errors in Mistranslated, and 5 errors in
Substituted Concept.
Meanwhile, human translator makes 8 errors. 1 error in Added and
Explicitated
Concept, and 2 errors in Omitted, Mistranslated, and Substituted
Concept.
Generally, it can be said that HT makes better performance than
IgT in translating
captions.
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ABSTRAK
VERONIKA, STEFANI. Instagram Translate’s and Human
Translation’s
Performance in Translating the Captions in @Basukibtp Instagram
Account. Yogyakarta: Program Studi Sastra Inggris, Fakultas Sastra,
Universitas Sanata
Dharma, 2017.
Aspek yang paling penting dalam penerjemahan adalah arti dari
Bahasa
sumber ke Bahasa target tersampaikan dengan baik. Penerjemahan
Bahasa tidak
hanya dilakukan oleh penerjemah profesional saja, tetapi juga
dapat dilakukan
oleh mesin penerjemah. Pada 2016, sebuah sosial media yang
dikenal dengan
Instagram memfasilitasi penggunanya dengan memberikan fitur
terjemahan pada
kolom takarir gambar. Namun, peneliti menemukan kesalahan
dalam
penyampaian makna dari bahasa Indonesia sebagai teks sumber ke
teks target
dalam bahasa Inggris. Kesalahan yang dimaksud, ditemukan pada
akun Instagram
milik seorang politikus Indonesia yang bernama Basuki Tjahaja
Purnama atau
biasa dikenal sebagai Ahok. Berdasarkan fenomena tersebut,
peneliti berkeinginan
untuk meneliti lebih lanjut hasil terjemahan yang dilakukan oleh
Instagram
Translate dan membandingkannya dengan hasil terjemahan
penerjemah
profesional.
Terdapat dua rumusan masalah yang menjadi fokus penelitian ini.
Masalah
pertama adalah menemukan kesalahan-kesalahan pada produk
terjemahan
Instagram Translate dan penerjemah profesional. Masalah kedua
adalah melihat
kinerja yang berbeda dari masing-masing terjemahan yang dapat
tercermin
melalui kesalahan-kesalahan yang dibuat.
Metode yang digunakan dalam penelitian ini adalah studi pustaka
dan
metode explicatory. Selanjutnya, metode kualitatif juga
digunakan untuk
menjabarkan hasil penelitian.
Hasil penelitian ini menunjukkan kesalahan-kesalahan yang
ditemukan.
Instagram Translate membuat sebanyak 54 kesalahan yang
diantaranya adalah 15
kesalahan pada konsep omitted, 6 kesalahan pada konsep added, 4
kesalahan pada
konsep untranslated, 24 kesalahan pada konsep mistranslated, dan
5 kesalahan
pada konsep substituted. Berbeda dengan penerjemah profesional
yang hanya
membuat 8 total kesalahan. Diantaranya 1 kesalahan pada konsep
added dan
explicitated, dan 2 kesalahan pada konsep omitted, mistranslated
dan substituted.
Secara garis besar, dapat dikatakan bahwa terjemahan yang
dilakukan oleh
penerjemah profesional menunjukkan kinerja yang lebih baik
daripada Instagram
Translate dalam menerjemahkan takarir gambar.
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CHAPTER I
INTRODUCTION
A. Background of the Study
Language as a means of communication plays an important role in
delivering
messages. People use language in many fields such as religion,
education,
technology, etc. However, according to Anderson cited in
(ethnologue.com), there
are more than 6800 distinct languages in the world. The fact
that a few people can
speak other languages besides their mother tongues is evident.
This case leads to a
development of technology that provides various features to help
its users deliver
and receive messages.
In this modern era, the use of technology gives significant
helps for its users,
including in language matter. The fact that technology is
consumed by a lot of
people makes it inseparable from the use of social media,
nonetheless. Through
technology, the users can provide and receive messages through
their own social
media accounts.
Social media have their revelatory role in delivering messages.
In social
media, people (users) can create and follow more than one
account. Hence, the
messages can be found in many accounts, whether the official or
non-official ones.
There are many politicians, artists, educators as well as
economists who share
messages through their own social media. One of the most popular
social media is
Instagram. Instagram provides its one core feature, which is to
share pictures,
besides videos, captions, likes, comments, Instagram Live and
Instagram Story.
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Instagram is one of social media which has global users. As
cited from
(Instagram-press.com), the Instagram users in 2017 are 800
million people around
the world. It makes a translation feature of Instagram Translate
(IgT) very useful.
Certainly, among other languages around the world, Indonesian
language is one to
be translated.
This undergraduate thesis discusses an official account which is
owned by
Indonesia politician, Basuki Tjahaja Purnama or commonly known
as Ahok. The
account, which has more than two millions followers, uploads
posts containing his
social and work life. Ahok’s Instagram account is chosen due to
the fact that he was
one of the governors who attracts Indonesia citizen’s attention
the most in these
past years. In addition, he is one of Indonesia politician who
delivers message and
information about issues in Indonesia through his own social
media, Instagram.
One of the captions in his posts on June 22 is, Selamat ulang
tahun Jakarta,
semoga terwujud Jakarta Baru yang modern, tertata rapi,
manusiawi dan
melayani.#JKT489 which is translated by IgT into “Happy
birthday, Jakarta,
hopefully come to Jakarta the new modern, neat, inhumane and
serve. #JKT489”.
From the translation, it is seen that the source text meaning
undergoes change (ST)
in target text (TL). In Indonesian, the word manusiawi has a
positive meaning but
then it is translated into exactly the opposite meaning,
“inhumane”.
From this case, it is interesting to closely examine how IgT
works, and what
errors might be found. According to Shivali, IgT started showing
its translation tool
in June 2016 (2016). Therefore, its relative novelty as a
machine translation (MT)
compared to other established MTs, such as Google Translate and
Bing Translator,
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is worthy of analysis related to what extent this new MT is able
to render the ST
into TT successfully.
The undergraduate thesis focuses on the translation result done
by IgT.
According to Newmark, translation itself is, “a craft consisting
in the attempt to
replace a written message and/or statement in one language by
the same message
and/or statement in another language (1981:7). In other words,
translation is a
process of replacing source text to target text by substituting
the language demanded
without changing the intended message. In order to analyze the
translation result,
the researcher would like to compare IgT to the manual
translation which is done
by human translator.
The manual translation is produced by a professional and
certified translator
who is the Secretary of Himpunan Penerjemah Indonesia
(Indonesian Translators’
Association), working in Translexi, a professional translator
services, Yogyakarta.
The fact that this research has its focus on the both
translation results, the
categorization of translation mistakes or errors is needed. In
addition, the
explanation and discussion to show the different results they
make are also required.
As for the researcher decides to conduct this research, it is
expected that this
undergraduate thesis would help the readers, especially students
in Universitas
Sanata Dharma, see the different performances of human
translator and Instagram
Translate in delivering message from Indonesian to English
captions. The
researcher also anticipates for the Instagram Translate and also
human translator to
develop their translation in accordance with the errors found by
the researcher.
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B. Problem Formulation
There are some problems in this undergraduate thesis which would
be
analyzed. They are formulated into two major problems as
follows:
1. What errors are found in the English translation of
@basukibtp Instagram
account done by Instagram Translate and human translator?
2. How do Instagram Translate and human translator perform in
translating the
captions in @basukibt Instagram account?
C. Objectives of the Study
This research aims to find and analyze the translation errors
made by both
Instagram Translate and human translator in translating
Instagram’s captions. The
errors are used as a source to measure translation performances
and to show the
difference result in translating captions.
D. Definition of Terms
In order to have the same perceptions and terminologies used in
this study,
the researcher defines the following terms:
Translation, as stated by Bell, “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).
Machine Translation, according to Hutchin, “is the now
traditional and
standard name for computerized systems responsible for the
production of
translations from one natural language into another, with or
without human
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assistance”. There are many machine translations available
today, to mention some
are Google Translate, Bing Translator, Sederet.com, and
relatively new Instagram
Translate (1992: 3).
Error Analysis in Machine Translation is “the identification
and
classification of individual errors in a machine translated
text. It is also a means to
access machine translation output in qualitative terms, which
can be used as a basis
for generation of error profiles for different system” (Stymne
and Ahrenberg, 2012:
1).
Instagram is “a relatively new form of communication where users
can
easily share their updates. It is a popular photo (video)
capturing and sharing mobile
application, with more than 150 million of registered users
since its launch in
October 2010” (Hu, Manikonda and Kambhampati, 2013: 2).
Instagram recently
released its machine translation, popularly called Instagram
Translate to help its
various users understand each other despite the language
barrier.
Caption, as defined by Oxford Advance Learner’s Dictionary, is
“words that
are printed underneath a picture, CARTOON,etc. that explain or
describe it” (2010:
209).
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CHAPTER II
REVIEW OF LITERATURE
In this chapter, the researcher aims to show and elaborate the
researches on
similar topic done by other researchers. This present research
complies with
Kurnianto’s and Febriana’s theses and also two journals written
by Li, Graesser and
Cai and also Saffari, Sajjadi and Mohammadi. Each of them is
reviewed to find the
similarity and difference discussion in order to avoid topic
duplication. Some
theories applied are also reviewed in order to set a solid
ground on which this
research has the focus on.
A. Review of Related Studies
1. Kurnianto’s thesis “ Google Translate Assessment With Error
Analysis:
An Attempt To Reduce Error”
This undergraduate thesis discusses the machine translate
assessment done by
Google Translate in three kinds of texts. There are an iPad user
guide, a National
Geographic article and Ownership Agreement document.
This research focuses on two problems. The first is to find what
errors exist
in the result of translation and the second is to suggest effort
to reduce all errors
found in the first problem. In analyzing the errors, the
researcher uses Koponen’s
first error category which divides the errors into six
subclasses. After all errors are
categorized, the researcher uses Farrús’s suggestion in three
methods to attempt
reducing errors.
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This thesis shows that in the final result there are 206 errors
in the three texts.
The suggestions by the researcher shows the errors in three
texts are reduced. The
first method, typing in isolated form, can reduce almost 50%
errors for omitted,
untranslated, and mistranslated concept. The second method, text
edition, reduces
errors related to orthographic errors and literal translation.
And the last method,
combination of all method, can reduce all categories.
This present undergraduate thesis develops Kurnianto’s thesis
and answer
questions not elaborated in his thesis. Although it uses similar
errors category, this
present undergraduate thesis does not stop in measuring machine
result. It continues
in giving a comparison to a manual translation which is done by
human or a
professional translator.
2. Febriana’s thesis “The Translation Performance of Sederet.com
And
Google Translate: A Comparative Study With Error Analysis”
In this undergraduate thesis, Febriana assesses two machine
translates which
are Sederet.com and Google Translate to translate three short
stories namely Dads
Blessings, The Grasshopper and the Ant, and The Princess and the
Pea. Similar to
Kurnianto’s thesis, this thesis also uses Koponen’s error
category in analyzing the
three short stories.
The errors found in the three texts translated by both MTs are
classified into
six subcategories. They are Omitted Concept, Added Concept,
Untranslated
Concept, Mistranslated Concept, Substituted Concept and
Explicitated Concept.
This research finds out 387 errors made by MTs and 69.5% errors
are in the
Mistranslated Concept category which is followed by Omitted,
Added and
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Untranslated Concept. In this thesis, the errors categorized are
then used to measure
the MTs performance in translating. Overall, by counting the
errors, this research
tells that Sederet.com performs better than Google
Translate.
As this undergraduate thesis shares the similar error category
by Koponen to
the present undergraduate thesis, the difference is also found.
The difference lies on
machine translation which is compared. In this present
undergraduate thesis, the
machine translation analyzed is Instagram Translate and instead
of comparing it to
another MT, the researcher chooses human translation as the
comparison.
3. Li, Graesser and Cai Journal “Comparison of Google
Translation with
Human Translation”.
In this journal, Graesser and Zhiqiang Cai investigate the
accuracy of Google
Chinese-to-English translation from the perspective of formality
and cohesion with
two comparison. They are Google translation with human expert
translation and
Google translation with Chinese source language. The text used
is a collection of
289 spoken and written texts excerpts from the Selected Works of
Mao Zedong in
both Chinese and English version.
The texts taken are analyzed by automated text analysis tools.
They are
Chinese and English Linguistic Inquiry and Word Count (LICW) and
the Chinese
and English Coh-Metrix (a computational tool). The result of
this study shows that
correlations on formality and cohesion showed Google English
translation is highly
correlated with both human English translation and the original
Chinese text.
The present study shares similarity to the study conducted by
Graesser and
Zhiqiang Cai which is to compare a machine translate and human
translator.
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However, besides the object, the machine that is compared show
differences. The
object used by present researcher is captions from Instagram. In
addition the
machine analyzed is not Google Translate but Instagram
Translate. The languages
analyzed are also differ, the present research uses Indonesian
and English while
Graesser and Zhiqiang Cai use Chinese and English. This present
research develops
the previous research by conducting analyses on the pattern and
giving possibility
reasons (of errors) to evaluate the translation being
compared.
4. Saffari, Sajjadi and Mohammadi “Evaluation of Machine
Translation
(Google Translate vs. Bing Translator) from English into Persian
across
Academic Fields”
In this study, the researchers investigate English-to-Persian
accuracy of
machine translation at lexical, semantic and syntactic levels by
using Groves and
Mundt (2015) Model of error taxonomy. The texts selected are
from 4 (four)
domains, namely law, literature, medicine and mass media. 60
texts from each
domain are taken. The texts translated by Google Translate, Bing
Translate as well
as human translators are evaluated with respect to lexical,
semantic and
grammatical accuracy.
The result of this study shows that the translation done by the
four human
experts just served as the benchmark against which Google and
Bing translation are
judged and scored. The machines translations are not yet as
accurate as human
translation in translating texts from English into Persian
translation.
The present study shares similarity of comparison, machine
translate and
human translator. However, the machine compared are Google
Translate and Bing
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Translate while the present study only focuses on one machine,
which is Instagram
Translate. Another difference lies on the number of human
translator, instead of
having 4 human translators, the present study has one human
translator only to
balance the comparison, one from machine and one from manual
(human)
translation. The present researcher agrees with the accuracy
being compared, which
is lexical. As for semantic and syntactic are not yet suitable
for the Instagram
Translate which is a relatively new machine translate to focus
on.
B. Review of Related Theories
1. Theories of Translation
There are many definitions about translation. Each expert has
his or her
definition based on his or her own perspectives. However, the
researcher would like
to see the relations among those experts’ definitions. The first
definition is
according to Catford, who states “the replacement of textual
material in one
language (SL) by equivalent textual material in another language
(TL)” (1965: 20).
Here, according to Catford, translation concerns with the text
material which
must be similar between the source text and target text. Even
though Catford only
focuses on the text material which is hard for the translator to
change the text
material while not to focus on the structure and meaning, Nida
and Taber support
the definition as
Translating consists of reproducing in the receptor language the
closest
natural equivalent of the source language message, first in
terms of meaning
and secondly in terms of style (1974:14).
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From the definition according to Nida and Taber, the translation
should be
the closest natural equivalent in meaning of the source language
message. It can be
detected by the target language readers that should have similar
response to the text
as in the source language readers have.
The last definition is according to Bell. Bell states that
translation is “the
expression in another language (or target language) of what has
been expressed in
another, source language, preserving semantic and stylistic
equivalences.”
(1991:5). Bell supports Nida and Taber’s definition in term of
semantic or meaning
and stylistic or style in language. From those definitions, it
can be concluded that
translation is a replacement of text material that concerns with
meaning and also
style. Certainly the text material should bring the same meaning
which indicated by
the same response from target text and source text’s
readers.
2. Machine Translation
According to Hutchin, machine translation is
the now traditional and standard name for the computerized
systems
responsible for the production of translation from one natural
language into
another, with or without human assistance (1992: 3).
Hutchin also states that in the twentieth century, humanity’s
oldest dream of
mechanization of translation has become a reality in which
computer programs are
capable to translate a wide variety of texts from one language
into another natural
language. Even though in reality, the translation is not always
perfect, however
humans have already achieved the development of program in which
MT “can
produce ‘raw’ translation of texts in relatively well-defined
subject domains, which
can be revised to give good-quality translated texts”. (Hutchin,
1992 : 1).
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According to Chéragui (2012: 165-166), Machine Translation has
four
different types. They are Machine Translation for Watcher
(MT-W), Machine
Translation for Revisers (MT-R), Machine Translation for Authors
(MT-A), and
Machine Translation for Translators (MT-T). MT-T is usually
incorporated into the
translation work stations and the PC based translation tools. It
aims at helping
human translators do their job by providing online dictionaries,
thesaurus and
translation memory.
In conclusion, machine translate is a machine that is used to
translate texts
with or without human by providing dictionaries, thesaurus and
translation
memory.
3. Human Translation
According to Hopkinson (2009: 1-2), the role of a translator is
many-faceted.
He or she must hear the music of the original, and replay it for
a new audience; a
good translation sings, and displays a rhythm that not only
reflects the original text’s
origin but also beats to a new drum.
In other words, a translator is both the reader and writer. The
translator must
transfer the meaning through message in a good way which
reflects the original
message. Hopkinson adds that in translating a literary works,
the translator’s job is
to “recreate this work of art sensitively and seamlessly in such
a way that it is true
to the original, as well as being equally enchanting, poetic and
perceptive”.
Therefore, a translation should have the same virtues as the
original, and inspire the
same response in its readers.
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4. Koponen’s Classification in Assessing Machine Translation
As stated by Hutchin (1992:2), that “a good translation which
are produced
by both human and machine is a difficult concept to define
precisely”, the error
category assessment is needed to help assessing translation
quality. According to
Koponen (2010), translation quality assessment is important for
both human and
machine to bring out interesting differences translation result.
Furthermore, the
analysis should separate form and function, and focus on errors
affecting the
accuracy of meaning (translation errors) over errors related to
fluency only
(language errors) such as capitalization and punctuation.
Koponen defines errors category based on the tested text into
two major
classes. They are namely relation between source and target
concepts and relation
between concepts. The two major classes are then divided into 6
and 8 subclasses.
The mismatches are as follows:
a. Relation between source and target concepts (Koponen, 2010:
4-5)
i. Omitted concept happens when Source Text (ST) concept that is
not
conveyed by the Target Text (TT). It means the concept that
should appear
in TT is absent. For example, when the ST Jakarta Barat then the
TT
translation shows Jakarta only, the concept of barat is
omitted.
ii. Added concept exists when TT concept that is not present in
the ST. This
category is exactly the opposite of Omitted Concept. TT adds new
concept
that is absent in ST. For example, the ST Terima kasih, bapak
Jokowi is
translated into “Thank you, Mr. Jokowi and wife”. From the TT,
the
additional of the word “wife” is categorized as Added
Concept.
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iii. Untranslated concept is Source Language (SL) words that
appear in TT. It
means that a word or some words from ST is not translated to the
TT. For
example, Bersama Keluarga Besar Damkar is translated into
“With
Damkar big family”. The word Damkar is categorized as
Untranslated
Concept because it appears in TT as the SL.
iv. Mistranslated concept happens when a TT concept has the
wrong meaning
for the context. In this category, the wrong meaning of a
concept possibly
happens because the word has polysemous meaning. For instance,
Dapat
laporan dari warga is translated into “Can report from citizen”.
The word
dapat means both “to get” also “be able” (can).
v. Substituted concept happens when TT concept is not a direct
lexical
equivalent for ST concept but can be considered a valid
replacement for
the context. For example, the ST Peresmian RPTRA di Menteng
is
translated to “Grand opening of RPTRA in Menteng”. The word
Peresmian is not a direct lexical equivalent to “Grand opening”
but
“inauguration”. As for “grand opening” is a direct lexical
meaning to
pembukaan perdana.
vi. Explicitated concept exists when TT concept explicitly
states information
left implicit in ST without adding information. In other words,
the concept
appears in TT does not change the meaning as a whole but giving
the
information more explicit than the ST intended to. For example,
the
addition of ohjelma or program to Norton Antivirus (Koponen,
2010:5).
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b. Relation between concepts (Koponen, 2010: 6)
i. Omitted participant happens when ST relation not conveyed by
the TT due
to an omitted head or dependent.
ii. Omitted relation is ST relation not conveyed by the TT due
to morpho-
syntactic errors that prevent parsing the relation although both
concepts
are present in the TT.
iii. Added participant is the TT relation not present in ST
introducing an added
concept.
iv. Added relation happens when TT relation not present in ST
arises due to
morpho-syntactic errors.
v. Mistaken participant exists when head or dependent of the
relation
different in ST and TT, not same entity.
vi. Mistaken relation is when relation between two concepts
different in ST
and TT, changed role.
vii. Substituted participant exists when head or dependent of
the relation
different in ST and TT, same entity.
viii. Substituted relation is relation between two concepts
different in ST and
TT, same semantic roles.
As it is categorized above, the relation between source and
target concepts or
Individual Concept are only represented by content words such as
noun, verb,
adjective and adverb. They can be unit larger than individual
words, for example in
the case of compound nouns, names and idioms, while relation
between concepts
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or Relation, are expressed through function words, inflection
and word order
(Koponen, 2010: 3)
C. Theoretical Framework
The definitions of translation, machine translation as well as
human translator
are used as the basic understanding to the topic being discussed
in this research.
After knowing the definitions, the theory proposed by Koponen on
error analysis is
used to identify the errors done by Instagram Translate and also
human translator.
It is done to answer the first problem formulated. In
determining the errors, only
the first category, Individual Concept, is used. This category
is represented by
content words, which are verb, adverb, adjective and noun. After
that, the result in
the first problem is used to measure the total errors to answer
the second problem.
The errors that have been categorized are looked deeper by the
researcher to see the
different performance through the errors pattern.
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CHAPTER III
METHODOLOGY
This chapter presents the methodology used in order to answer
two problems
formulated. There are four parts discussed in this chapter,
namely area of research,
object of the study, method of the study and research procedure.
In the research
procedure, it discusses other four parts namely types of data,
data collection,
population and sample, and data analysis.
A. Areas of Research
This study takes the translation of Instagram Translate and
professional
translator. It focuses on the texts which are captions
translated by Instagram
Translate and human who is a certified translator. It also
evaluates the translation
from Indonesian language to English using translation quality
assessment. There
are three approaches of translation quality assessment, one of
them is target-
language oriented. In accordance with Williams and Chesterman
“the relation at
stake is not with the source text but with the target language,
equivalence is not a
central concept here. The idea is to measure the translation’s
degree of naturalness”
(2002:8).
The researcher aims to find out the mistakes or errors in
translating the
captions. By finding the errors, the researcher is able to
compare the performances
of IgT and HT.
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B. Object of the Study
This undergraduate thesis was to compare the translation of
Instagram
Translate and human translator. Therefore, the focus was on the
target text which
was English. The data collected as the object of the study were
captions. They
consisted of word, phrase and sentence taken from an official
and certified account
of @basukibtp captions in Instagram. The data were then
translated by Instagram
Translate and a professional translator.
C. Method of the Study
This undergraduate thesis applied library and explicatory
methods. The
library method “involves identifying and locating sources that
provide factual
information or personal/ expert opinion on research question;
necessary component
of every other research method at some point” (George, 2008:6).
This method was
applied to gather the information and theories of translation,
both human and
machine translation, and linguistics aspects in order to be able
to analyze the data.
Meanwhile, in accordance with George, the explicatory method
entails a careful, close, and focused examination of a single
major text, or of
evidence surrounding a single complex event, in attempt to
understand one or
more aspect of it (2008:6).
This method was applied to examine the captions closely and
carefully, as the
captions were the focus of this research. It was also done to
indicate the errors and
to compare the errors found in the captions. Furthermore, the
qualitative research
method was also applied. Qualitative “designates any research
whose results are
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captured in words, images, or non-numeric symbols” (George,
2008:7). It meant
that in analyzing the data, the researcher used descriptive
explanation to
demonstrate the findings.
The data in this undergraduate thesis were primary data. In
other words, the
data taken were not collected from the other studies or
researchers. The data were
collected by the researcher from the caption in @basukibtp
official account in
Instagram.
D. Research Procedure
1. Types of Data
The objective data collected in this undergraduate thesis were
taken from a
ST and TT. The ST were captions in @basukibtp Instragram’s
account, which were
collected from March until September 2016. The TT were the
translation from 21
captions collected which had been translated by Instagram
Translate and a
professional translator. As the source text, the captions
collected consisted of 382
words which not all of them were in the form of sentences but a
word. Meanwhile
for the TT, after the captions were translated, the amount of
words were differ. The
captions translated by Instagram Translate consisted of 424
words and human
translator obtained more words which were 478.
2. Data Collection
In collecting the data for this undergraduate thesis, the
researcher took several
steps in order to get completed and sufficient data. Firstly,
the researcher chose a
verified account in Instagram. The fact that Instagram
Translates’ result would be
analyzed, it was a must for the researcher to take texts from
captions in Instagram.
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In order to have a reliable caption, a verified account was
selected. A verified
account, @basukibpt, showed that the owner follows the Community
Guidelines.
Secondly, the captions collected were taken from March 2016
until
September 2016. They were collected from March 2016 since the
See Translation
button in that account only started its availability from
February. However, the
captions from February were not taken due to unavailability some
captions to show
See Translation button. The fact that @basukibtp created the
posting randomly, it
meant that the account owner had no regular schedule in
uploading the posts.
Therefore, the researcher did not take all the caption created.
The researcher decided
to collect the data in the first, middle and the end of the
months in order to have the
same number of data taken in each month.
Thirdly, after all the caption collected, the researcher tapped
the captions See
Translation button in Instagram to see the translated captions.
Because this
undergraduate thesis aimed to compare human and machine
translation result, the
next step done by the researcher was to ask a professional
translator to translate all
those 21 captions. The person asked to translate was a
professional translator who
had been professionally dealing with translation since 2010. She
was working in an
international translation house. Afterwards, the translation
done by human and
machine was put in the table. The table was arranged in such a
way so that each
caption would be side by side with its translation. The table
was displayed as
following.
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Table 1 Example of the Data Coding
No. ST No. Instagram
Translate No.
Human
translation
1/ST/II/25
Disambangi
Ganjar
Pranowo
dan Ridwan
Kamil di
Balaikota
DKI
1/TT/II/25/IG
Disambangi
ganjar
pranowo
and ridwan
kamil in
central unit
1/TT/II/25/H
Got Visited
by Ganjar
Pranowo
and Ridwan
Kamil at
DKI’s City
Hall
The code can be read as follows
1 : The number of the caption
ST : Source Text
II : The month of the caption created, II means February
25 : The date of the caption created
TT : Target text
IG : The caption are translated by Instagram Translate
H : The caption are translated by human translator
3. Population and Sample
According to Hanlon and Larget, a population “is all the
individuals or units
of interest” to a researcher. The population taken was a total
of individual that has
certain characteristic in dealing with the researcher’s topic
research. The data
population of this undergraduate thesis were all captions
collected from February
2016 until September 2016. However, the researcher found that
the caption created
by @basukibtp in the Instagram account did not share the same
number of postings
every month. Therefore, sample was needed in this research.
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In accordance with Hanlon and Larget, a sample “is a subset of
the
individuals in a population”. Sample was selected from
population to represent the
population as the larger set. In this research, the purposive
sample was chosen. As
cited from (research-methodology.net), purposive sampling is “a
sampling
technique in which researcher relies on his or her own judgment
when choosing
members of population to participate in the study”.
All the captions collected had the same characteristic to the
population.
Therefore, instead of taking all the captions, the researcher
used the purposive
sampling in deciding captions to select. For the result, the
researcher took 3 captions
each month, starting from the first, the middle to the end of
the month. For the
caption picked as the first month, it should be the caption that
was posted in the
very beginning in the month and for the end of the month caption
should be posted
in the very late of the month. Meanwhile, for the middle month,
the caption taken
should be posted exactly or nearly the date of 15. This sample
was conducted to
avoid dissimilarity amount of caption collected from February
until September.
When conducting the sample purposive, the researcher found some
captions In
February did not show the See Translation button. Therefore, the
sample was 21
captions, taken from March until September 2016.
4. Data Analysis
There were two steps used in order to answer problems
formulated. First was
to find the error done by Instagram Translate and the
professional translator. The
researcher used Koponen’s error categories focusing on
individual concepts. The
error categories based on Koponen’s journal were consisted of
Omitted Concept,
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Added Concept, Untranslated Concept, Mistranslated Concept,
Substituted
Concept and Explicitated Concept. After analyzing the data,
every datum would be
grouped based on its categories.
As stated above, that the focus of this research was on the
first category of
error analysis. It was Individual Concept. The consideration in
choosing the first
category because Instagram Translate was a relatively new
feature of translation
tool which just been launched in 2016. Meanwhile, the data
collected and analysis
were also started at the same year. The researcher believes that
it was not suitable
to expect such a new tool to perform immaculately. For the
second category needed
more advanced translation result. Therefore, the category used
was only the first
one since it was focus on the individual concept, how one
concept was conveyed in
another language. In addition, the area to analyze was also
limited, content words.
Data that had been collected were categorized and examined based
on the
result of both translation. In order to find out the source of
error, the analysis process
requires alternative translation so that the error can be
compared. The alternative
given was needed to give an organized analysis of each error.
However, the
alternative translation was not given side by side to the
translation result done by
either IgT or HT. It would be given in the explanation. To give
a clearer explanation,
the example was on the table below.
Table 2 Description of error in Instagram Translate
No. ST No. TT Type of
Errors
6/ST/I
V/29
Peresmian RPTRA di
menteng dalam, tebet…
6/TT/I
V/29/
IG
Grand opening in rptra
in menteng, tebet.
Omitted
Concept-noun
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Table 3 Description of error in Human Translator
No. ST No. TT Type of
Errors
7/ST/
V/1
Kondangan tadi siang di
Cipinang Lontar …
7/TT/
V/1/H
Attending a wedding
reception in Cipinang
Lontar …
Omitted
Concept-adverb
In order to answer the second problem of this undergraduate
thesis, the errors
found in the captions would be counted according to each
category. The errors
found by Instagram Translate would be separated from the errors
made by human
translator. After all errors were counted as each subcategory,
the number would be
summed up as the total number of errors in both machine and
human translator.
This number would lead the researcher draws a pattern of errors
and help researcher
to consider the conclusion of this research.
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CHAPTER IV
ANALYSIS RESULTS AND DISCUSSIONS
In this chapter, the discussion is presented as the result of
data analysis. The
discussion covers the two problems formulated in the previous
chapter. The first
one covers the errors in each category. There are two big
classes of analysis result
from the first problem namely errors done by IgT and the latter
is done by HT. Each
class has other categories error which are followed by table or
diagram before its
elaboration. As for the following problem, the discussion is
based on the first
problem result. It is done in order to look at the pattern of
typical errors made by
different translation.
A. The Errors Found in @basukibtp Instagram Captions
The first discussion in this analysis is to find the errors in
Instagram captions
translated by Instagram Translate. All captions translated into
English are analyzed
and categorized according to Koponen’s category on error
analysis. As explained
in the previous chapter, the individual concept error which has
six subcategories is
the focus in this research. In order to see the number of each
error subcategory,
table and diagram are provided before discussion. In this
chapter, every error found
is not discussed. However, some examples are given to present
each subcategory
error.
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1. The Translation Errors by Instagram Translate Found in
@basukibtp
Captions
The researcher finds out that errors made by IgT only occur in
five out of six
categories. They are Omitted Concept, Added Concept,
Untranslated Concept,
Mistranslated Concept, and Substituted Concept.
a. Omitted Concept
A translation error is categorized as omitted concept when “ST
concept that
is not conveyed by the TT” (Koponen, 2010: 4). It means that the
concept from ST
does not exist in TT. The absence of the concept has a huge
possibility to cause the
information in TT incomplete and make the meaning in ST not
totally delivered. In
the individual concept, the words analyzed are the content words
(Koponen, 2010:
3). In this category, the errors are only found in three
subcategories, namely Omitted
Concept of noun, verb, and adjective. IgT makes 15 out of 54 of
errors found. The
most frequent error existed in Omitted Concept is the omission
of noun which is
followed by adjectives and verb. The table below shows that the
errors in Omitted
Concept consist of 11 nouns, 3 Adjectives, and 1 verb.
Table 4 Omitted Concept by Instagram Translate
11
1
3
00
2
4
6
8
10
12
Noun Verb Adjective Adverb
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i. Omitted Noun
In Omitted Concept of noun, there are 11 errors found. The table
below shows
the representative of Omitted Noun errors.
No. ST No. TT Type of
Error
11/ST/
VI/14
… dua cumi cumi datang
mengganggu …
11/TT/
VI/14/
IG
… two squid with
come bother …
Omitted
Concept-
plural noun
15/ST/
VII/19
… Ayo daftar magang di
Kantor Gubernur DKI …
15/TT
/VII/
19/IG
… Let's list of
internships in the
office of Jakarta …
Omitted
Concept-noun
The concept of plural noun as seen in the datum 11/ST/VI/14 is
not conveyed
by IgT. In the TT, the noun cumi-cumi or squids is preceded by
determiner two
which indicates plurality. However, IgT fails to translate one
of the indications of
noun plurality by omitting the letter s in the noun “squid”.
According to Oxford
Dictionary, the plural squid can be written as squid or squids.
The researcher takes
the second spelling which is squids to examine this datum. The
fact that a noun is
modified by determiner (number) makes the result of IgT’s
translation irrelevant to
the noun mentioned. Therefore, the translation of dua cumi-cumi
should be two
squids.
As for the datum 15/ST/VII/19, the noun Gubernur or governor is
not
mentioned in the TT. As in ST, the word Gubernur has a function
to specify the
“office” as its head. Even though the word Gubernur performs as
a modifier in ST,
but IgT is considered failed to translate a word Gubernur as the
word itself stands
as a noun. In ST, the caption tells about the Governor’s Office
while IgT only
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translates the “office” which leaves the information incomplete.
The omission error
causes generalization for the TT readers since the office
mentioned is not specific
yet. Therefore, the translation of Kantor Gubernur should be
Governor’s office.
ii. Omitted Verb
In the Omitted Concept, there is only 1 error found in Omitted
Verb. The table
below shows the only datum found as error.
No. ST No. TT Type of
Error 13/ST/
VII/5 Pemerintah
menetapkan 1 Syawal
1437 H jatuh pada
esok hari, …
13/
ST/VII
/5/IG
The Government
Assign 1 Shawwal
1437 h on tomorrow,
…
Omitted
Concept-
verb
In the datum 13/ST/VII/5, the verb jatuh is not conveyed in the
TT. In the ST,
the verb expresses an action as the main verb to convey the core
information in the
datum above. This error happened is not caused by the
incompetent of IgT to find
the equivalence translation of word jatuh since IgT can
translate the exact same
word correctly as “fall”. Therefore, the omission affects, of
course, the ST intended
meaning. The result in TT brings incomplete information that
make the inequality
exist in ST and TT. Therefore, this verb which plays as an
action verb should not
be omitted but translated into “falls” or “is”.
iii. Omitted Adjective
In the Omitted Adjective, there are 3 errors found. However,
there is only 1
datum is given below as a representative of 3 errors of Omitted
Adjective.
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No. ST No. TT Type of
Error
8/ST/V
/15
...Jakarta Barat
daerahnya kotor…
8/TT/
V/15/
IG
...Jakarta area
absolutely gross…
Omitted
Concept-
adjective
The concept of adjective Barat or west is not conveyed in TT.
Meanwhile,
the function of the adjective is to modify the noun, Jakarta. It
is supposed to describe
which Jakarta the ST refers to since Jakarta is divided to 5
regions, namely North,
West, South, East and Central Jakarta. By omitting the
adjective, the noun becomes
more general than it is intended to be. The TT has lost the
additional information of
the Jakarta referred to, which is the West one. This omission
does not cause much
changes in the sentence, but the intended specific meaning from
ST. Therefore, the
word Barat should not be omitted but translated into West or
left untranslated
because it is a proper name.
b. Added Concept
Added Concept is an error that exists when “TT concept that is
not present in
the ST” (Koponen, 210:4). In means that the TT adds information
which is not
intended or presented by ST. As the result, IgT makes 6 errors
of Added Concept.
The most occurred error is in addition of noun category and the
rest categories just
have slightly different in number of errors.
Unlike the Omitted Concept category, Added Concept has less
number of
errors and sub categories. However, the most frequent error
existed is similar to the
Omitted Concept, which is noun category. It has 50% from 6
errors found. The table
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below indicates that the Added Concept error found consists of 3
nouns, 2 adverbs,
and 1 adjective.
Table 5 Added Concept by Instagram Translate
i. Added Noun
The errors found in this concept are 3. The errors exist in
datum 4/ST/IV/1,
7/ST/V/1, and 12/ST/VI/30. The table below shows one of the
errors found.
No. ST No. TT Type of
Error
12/
ST/VI/
30
Amin, terima kasih atas
ucapan dan doanya ...
12/TT/
VI/ 30/
IG
Amen, thank you for
the birthday wishes …
Added
Concept-
noun
In this case, at a glance, the addition of the word “birthday”
might be said not
totally wrong. This is due to the fact that the day when the
posting is uploaded, that
is also one day after @basukibtp’s birthday. The additional of
the word “birthday”
can be considered relevant since the wishes that refer to are
actually birthday
wishes.
However, the word “birthday” or hari ulang tahun in the ST does
not exist
and appears in the TT. In this case, by only looking at the
caption or without the
picture, the addition of “birthday” is considered as an error.
IgT fails to translate its
3
0
1
2
0
0.5
1
1.5
2
2.5
3
3.5
Noun Verb Adjective Adverb
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intended meaning from ST, which is “wishes” or doanya. The
additional of
“birthday” has no relation to the word wishes because wishes
alone is not the pair
of the word birthday, such as the words “not only” to its pair
“but also”.
As the result, the word “birthday” is categorized as an error
due to the analysis
focuses on the caption. There is an additional information that
does not present in
the ST. Therefore, the translation of terima kasih atas ucapan
dan doanya should
be “thank you for the greetings and wishes”.
ii. Added Adjective
There is only 1 error found in Added Adjective. The datum
18/ST/VIII/23 is
given to give a clearer comparison between message in ST and
TT.
No. ST No. TT Type of
Error
18/ST/V
III/23
Pagi tadi bersama warga
di peresmian Ruang
Publik Terpadu Ramah
Anak (RPTRA) …
18/TT
/VIII/
23/IG
This morning with the
residents at the
national integrated
public space (child
friendly) …
Added
Concept-
adjective
The word “national” according to Oxford Dictionary, is either
“owned or
controlled by government” or “connected with a particular
nation”. However, in
the ST, that concept does not exist but appears in the TT. The
addition of the word
“national” in TT creates meaning for the TT readers that the
concept of national
plays a role as the modifier of RPTRA (Ruang Publik Terpadu
Ramah Anak) or
Child Friendly Integrated Public Space in the caption. It adds
the concept that the
RPTRA is owned/controlled by government. Meanwhile, the concept
is absent in
ST. Consequently, the concept of adjective national should not
be present in the TT.
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iii. Added Adverb
There are 2 errors found in this concept. The first error is
found in datum
8/ST/V/15 and the last one is in datum 11/ST/VI/14. The table
below shows one of
the errors mentioned.
No. ST No. TT Type of
Error
8/ST/
V/15
Dapat laporan warga
kalau Kapuk, Jakarta
Barat daerahnya kotor …
(RPTRA) …
8/TT/
V/
15/IG
Can citizens report if
ceiba, Jakarta area
absolutely gross …
Added
Concept-
adverb
The word “absolutely” in the datum 8/ST/V/15 shows that the
concept of
adverb is added in the TT. As seen in the ST, the adverb
“absolutely” is not found
in ST. However, IgT adds the adverb to describe the adjective
“gross” in the
translation result. According to Oxford Dictionary, absolutely
is “used with
adjectives or verbs that express strong feelings or extreme
qualities to mean
extremely”. The addition, in fact, gives unintended information
as an emphasis to
the word gross. As for the TT readers, this addition delivers
the stronger emphasis
compare to the ST intended. Moreover, the description of noun,
Kapuk in West
Jakarta, becomes dissimilar.
c. Untranslated Concept
Deriving from its name, Untranslated Concept is an error that
happens when
“SL words that appear in TT” (Koponen, 2010: 4). It means that
the word from ST
is kept untranslated in the TT. Instagram Translate makes 4
errors in Untranslated
Concept, which 3 of them are in untranslated of noun and 1 error
in untranslated
verb. The Untranslated Concept has less errors compare to the
other categories.
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From all the errors made by IgT, this concept makes 7.40% errors
out of 54. The
table shows that there are 3 nouns and 1 verb found in
Untranslated Concept error.
Table 6 Untranslated Concept by Instagram Translate
i. Untranslated Noun
In the Untranslated Concept, there are 3 errors found. The
errors exist in data
1/ST/III/1, 3/ST/III/27, and 11/ST/VI/14. The table below shows
the error
representative of this category.
No. ST No. TT Type of Error
1/ST/I
II/1
Bersama Keluarga
Besar Damkar
1/TT/I
II/1/IG
With big family
damkar
Untranslated
Concept-noun
In the datum 1/ST/III/1, the word Damkar appears as it is in TT.
Damkar as
the shortening word from Pemadam Kebakaran or Firefighter is
left untranslated
by IgT. As a blended word, IgT is considered failed to find the
equivalence form in
TT. Hence, when it happens to translate the words as Pemadam
Kebakaran, IgT
shows the better translation result. From this case, shortening
words are not
recommended to make a good translation by IgT.
3
1
0 00
0.5
1
1.5
2
2.5
3
3.5
Noun Verb Adjective Adverb
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ii. Untranslated Verb
Similar to the category of Omitted Concept, there is only 1
error found in the
verb category. The Untranslated Verb is the word dikasih in
datum 4/ST/IV/1. The
table bellows displays the error mentioned.
No. ST No. TT Type of Error
4/ST/I
V/1
Ekspresi ketika
mengharap dikasih
tempat duduk
4/TT/I
V/1/IG
Expression when hope
dikasih seats
Untranslated
Concept-verb
In ST, the word dikasih appears in the TT. IgT fails to
translate the passive
voice in the datum 4/ST/IV/1. The verb should be translated into
“is given” or “to
be given”. As there are two verbs in the datum above namely
mengharap or
expecting as an active verb and dikasih as a passive verb, the
error found could be
caused by the inability to translate the passive verb since the
active one is translated.
IgT fails to see both verbs to be translated. For dikasih is
another verb in datum
which is untranslated, it disables the TT readers to gain the
information of the
caption as a whole. The translation should be “The expression
when expecting to
be given a seat”.
d. Mistranslated Concept
Mistranslated Concept is an error when “a TT concept has the
wrong meaning
for the context” (Koponen, 2010: 4). It means the TT gets the
wrong concept from
the intended concept in ST. In this research, IgT translation
gives the out of the
context information from what the ST intends to convey. There
are 4 sub categories
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of error exist in Mistranslated Concept, namely Mistranslated
Concept of noun,
verb, adjective and adverb.
IgT makes 24 errors in Mistranslated Concept. The noun error sub
category
still shows the highest number of errors. Then, it is followed
by verb, adverb, and
adjective sub category as the least occurred errors. It is seen
in the table below that
in Mistranslated Concept, there are 14 nouns, 5 verbs, 2
adjectives, and 3 adverbs
found as errors.
Table 7 Mistranslated Concept by Instagram Translate
i. Mistranslated Noun
The most occurred error is found in Mistranslated Concept. From
24 errors
found in Mistranslated Concept, 14 of them exist in
Mistranslated Noun. The table
below shows the error as the representative of Mistranslated
Noun.
No. ST No. TT Type of Error
3/ST/III/
27 … di daerah Koja …
3/TT/II
I/27/
IG
… in the area of tree … Mistranslated
Concept-noun
In the datum 3/ST/III/27 the noun Koja is mistranslated. The
intended context
by ST should not be translated, due to the fact that Koja is a
proper noun. However,
IgT mistranslates the concept into “tree”, which is completely
not suitable for the
14
5
23
0
2
4
6
8
10
12
14
16
Noun Verb Adjective Adverb
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concept in the ST. By looking at the mistranslation of Koja as a
sub district in
Jakarta into “tree”, IgT is considered failed to pick the proper
meaning for a word
that has polysemous meaning. IgT picks the meaning of Koja as a
tree for the reason
that Koja is also kind of leaf that is used to cook. In here,
Koja refers to salam koja
or in Indonesia, it is also called daun kari (curry leaves).
However, instead of
translating Koja as curry leaves, IgT translates the
superordinate of it, which is tree.
This mistranslation, of course, creates the wrong information
for the TT readers.
ii. Mistranslated Adverb
There are 3 errors found in the Mistranslated Adverb. The errors
exist in data
number 2/ST/III/15, 20/ST/IX/17, as well as 21/ST/IX/30. The
table below consists
of mistranslated concept in one of the adverbs found.
No. ST No. TT Type of Error
2/ST/II
I/15
… Sekarang bikin SIUP
dan TDP online dan
simultan …
2/TT/II
I/15/IG
… Now to make a trade
operation permit and the
company registration
number online and
simultaneous …
Mistranslated
Concept-adverb
IgT mistranslates the adverb simultan or “simultaneously”
into
“simultaneous”. In the datum 2/ST/III/15, the concept of
simultan or
“simultaneously” is supposed to be an adverb that modifies the
verb, explaining
how to do a registration. In addition, there are two adverbs in
the caption above
namely “online” and simultan. From this case, IgT fails to
recognize the second
adverb and fails to translate the second adverb as it is
intended in ST. Therefore,
the translation of adverb simultan should be translated into
“simultaneously”.
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iii. Mistranslated Verb
In Mistranslated Verb, there are 5 errors exist. The table below
gives 2 data
to show the errors found.
No. ST No. TT Type of Error
8/ST/
V/15
Dapat laporan warga...
8/TT/
V/15/
IG
Can citizens report …
Mistranslated
Concept-verb
15/ST/
VII/19
… Ayo daftar magang di
Kantor Gubernur DKI …
15/TT/
VII/19
/IG
… Let's list of internships
in the office of Jakarta …
Mistranslated
Concept-verb
The mistranslated concept of verb in the datum 8/ST/V/15 exists
in the word
dapat (mendapatkan) or “to receive” or “to get”. This happens
because in Indonesia
language, dapat means both “to receive” and also “can” or “be
able to”. Looking at
the translation result, IgT fails to differentiate which
intended meaning the ST
would like to convey. Therefore, dapat is translated into “can”
instead of “to
receive”.
As in datum 15/ST/VII/19, the mistranslated concept is also
found. The word
daftar (mendaftar) or “to apply” is translated into the noun of
daftar which is “list
of”. According to Kamus Besar Bahasa Indonesia, daftar is a noun
and only stands
as a noun. However, in ST, it plays a role as a verb. From this
phenomenon, the
mistranslation of verb is supported by the mistake made by ST.
The word daftar
(noun) should be written as mendaftar (verb) if it has to stand
as a verb. Therefore,
IgT as a machine translate, certainly fails to read the intended
meaning.
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iv. Mistranslated Adjective
The least occurred error in Mistranslated Concept in
Mistranslated Adjective.
The errors found are 2. They exist in data number 10/ST/VI/1 and
21/ST/IX/30.
The table below shows one of them.
No. ST No. TT Type of
Error
21/ST/I
X/
30
… Mohon maaf
pelaksanaan MRT ini
membuat Jakarta tambah
macet …
21/
TT/
IX/30/I
G
… Sorry for the
implementation of this
make mrt jakarta add
crashed ...
Mistranslated
Concept-
adjective
Datum 21/ST/IX/30 shows that adjective macet or “jamming” is
mistranslated. The context presented in the ST is macet that
refers to a condition
where vehicle cannot move or traffic jams in Jakarta. However,
the result of
translation shows the message of having “crashed” which related
to vehicle and its
accident. TT provides the wrong information of the intended
concept, “jamming”.
Even though both macet and “crashed” are related to vehicle,
still, an accident as
described in TT is absent in ST. Therefore, macet should be
translated into “traffic
jams” so that the intended meaning of macet is conveyed.
e. Substituted Concept
An error is categorized as Substituted Concept when “TT concept
is not a
direct lexical equivalent for the ST concept but can be
considered a valid
replacement for the context” (Koponen, 2010: 4). It means that a
translation is not
considered as a completely out of the intended concept but
considered to share the
similarity from the ST. This error is not a fatal one since it
has the valid replacement
concept as a whole. In this category, IgT makes 2 sub categories
error which are
verb and noun. Unlike the other categories, Substituted Concept
has more number
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of errors in the verb rather than noun category. From all the
category made by IgT,
Substituted Concept has 9.25% of errors. The table below shows
that Substituted
Concept errors found are 2 nouns and 3 verbs.
Table 8 Substituted Concept by Instagram Translate
i. Substituted Verb
There are 21 data collected from Instagram’s captions and 3 of
them have the
word kondangan which all are found as Substituted Verb error
category. The table
below shows one of the words, kondangan, in the datum
8/ST/V/15.
No. ST No. TT Type of Error
8/ST/
V/15
… Hari ini pilih
kondangan ke daerah
Kapuk …
8/TT/V
/15/
IG
… Today choose
invitation to ceiba …
Explicitated
Concept-verb
One of the Substituted Verb exists in datum 8/ST/V/15. The verb
kondangan
is translated into “invitation”. At first glance, the result
might be categorized as
Mistranslated Concept. However, if it is analyzed deeper the
result of IgT is not out
of the intended concept from ST. The context is not wrong but
still related.
Moreover, the concept of “invitation” does not bring a new
information in TT.
2
3
0
0.5
1
1.5
2
2.5
3
3.5
Noun Verb
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Hence, the “invitation” and kondangan are regarded experiencing
Substituted
Concept. According to KBBI V, kondangan is “pergi menghadiri
undangan
perkawinan dan sebagainya (untuk mengucapkan selamat dan
sebagainya” or
going to attend wedding invitations and so forth (to
congratulate and so forth,
translated by researcher). Meanwhile in TT, the translation of
kondangan is
“invitation” which has a more general meaning as it stands as a
noun. However, it
still shares the information of inviting as mentioned in the
word kondangan. The
fact that the translation is still a valid replacement due to
the similarity of
information shared by both ST and TT, makes this error as
Substituted Concept.
The translation of kondangan for the intended meaning in the
datum above should
be “to attend a wedding reception”.
ii. Substituted Noun
Unlike the other categories which almost the most occurred error
is in noun
category, in Substituted Concept, noun category shows the least
occurred of errors.
They are 2 errors found in this category. The table below shows
one of them.
No. ST No. TT Type of Error
15/ST/
VII/19
… di Kantor Gubernur
DKI ...
15/TT/
VII/19
/IG
… in the office of
Jakarta ...
Substituted
Concept-noun
The initialism of DKI or Daerah Khusus Ibukota is undergone
Substituted
Concept of noun. DKI is supposed to be translated as either DKI
which is left
untranslated or Special Capital Territory. However, in the datum
above, IgT
translates the initialism into Jakarta. The fact is, in
Indonesia, the initialism of DKI
only stands for Jakarta as the capital city of Indonesia. It
means that DKI is kind of
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“title” because this city has its special role for Indonesia.
Therefore, DKI precedes
the noun Jakarta in order to show the city is distinct. This
makes the form of DKI
always refer to Jakarta. Even though the concept of DKI is not a
direct equivalent
for the concept of Jakarta, it is still considered as a valid
replacement.
2. The Errors Found in @basukibtp Instagram Captions by
Human
Translator
The errors found in Instagram Caption by Human Translator are
classified
into 5 categories. The categories are based on Koponen’s theory
on error analysis.
There are Omitted Concept, Added Concept, Mistranslated Concept,
Substituted
Concept and Explicitated Concept.
Table 9 Omitted Concept by Human Translator
The table above shows errors made by HT, 1 in noun category and
1 in verb
category.
a. Omitted Concept
In the Omitted Concept, there are two sub categories found.
There are omitted
adverb and verb. HT makes equal error of verb and adverb.
Omitted Concept has
25% errors out of 8 errors made by a professional
translator.
1 1
0 00
0.2
0.4
0.6
0.8
1
1.2
adverb Verb Adjective Adverb
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i. Omitted Adverb
In the Omitted Adverb, there is only 1 error found. The error of
this category
only exists in datum 7/ST/V/1. The table below shows the
translation done by HT
is categorized as Omitted Adverb.
No. ST No. TT Type of Error
7/ST/
V/1
Kondangan tadi siang
di Cipinang Lontar …
7/TT/
V/1/H
Attending a wedding
reception in Cipinang
Lontar …
Omitted
Concept-adverb
In the data above, there is an adverb which is omitted, tadi
siang or “this
afternoon”. The omission does not change the whole meaning as it
is intended in
ST. However, the omitted adverb by HT makes the meaning in TT
incomplete
because tadi siang stands as a complement to provide the
information when the
event happens. This omission error might not cause confusion but
incomplete and
different interpretation for TT readers. Therefore, the adverb
tadi siang should not
be omitted but translated into “this afternoon”.
ii. Omitted Verb
It is similar to the Omitted Adverb, the error found is only 1.
The table below
shows error exists in datum 4/ST/IV/1.
No. ST No. TT Type of Error
4/ST/I
V/1
Ekspresi ketika
mengharap dikasih
tempat duduk
4/TT/I
V/1/H