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D e n g a n d e m i k i a n s a y a m e m b e ir k a n k e p a d a P e r p u s t a k a a n U n i v e r s ti a s S a n a t a

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a n d M o t h e r M a r y b e c a u s e t h e y p e r m i t m e t o if n i s h t h i s if n a l t h e s i s . W ti h o u t t h e i r

b l e s s i n g I w o u l d n o t b e a b l e t o if n i s h m y w ir it n g . I w o u l d a l s o il k e t o t h a n k m y

l o v e l y p a r e n t s , w h o a l w a y s g i v e m e s u p p o tr t h r o u g h t h e i r p r a y a n d m a t e ir a l s ; m y

t h r e e s i s t e r s , w h o a r e a l w a y s f u n a n d e n c o u r a g i n g ; m y t w i n b r o t h e r , w h o a l w a y s

i n v ti e s m e t o c o m p e t e ; a n d e s p e c i a ll y m y g ri fl ir e n d , w h o a l w a y s a c c o m p a n i e s a n d

e n c o u r a g e s m e .

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f o r b e i n g m y a d v i s o r , w h o h a s l e d a n d g u i d e d m e i n if n i s h i n g t h i s w ir it n g a n d B u

A d v e n it n a P u rt a n it , S . S . , M . H u m . f o r b e - i n g m y c o a d v i s o r , w h o h a s r e a d a n d

g i v e n m e a l o t o f s u g g e s it o n s a n d c o r r e c it o n s s o m y w ir it n g w a s b e - tt e r a n d w e ll

i m p r o v e d . M y g r a t ti u d e a l s o g o e s t o M r s . F a r r ú s M ri e ri a , w h o w a s a b l e t o p r o v i d e

h e r it m e t o c o r r e s p o n d w ti h m e f o r g i v i n g v e r y u s e f u l a d v i c e .

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( m y s e c o n d h o m e ) , m y b e s t f ir e n d s i n E n g il s h L e tt e r s , c o ll e a g u e s f r o m L e m b a g a

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t h e s i m li a r r e s e a r c h i n t h e f u t u r e . A t h o u s a n d o f g r a t ti u d e i s g i v e n f o r e v e r y t h i n g .

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6 c it n a m e S .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

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  1 . 7 s n o it u l o S ’ s ú r r a F .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  8

  1 . C k r o w e m a r F l a c it e r o e h T .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . 19 Y G O L O D O H T E M

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  I R E T P A H C . A t f o t c e j b O y d u t S e h .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  2 . B y d u t S e h t f o d o h t e M .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  2 . C e r u d e c o r P h c r a e s e R . 1 a t a D f o s d n i K .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  3

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  2 . 2 e l p m a S d n a n o it a l u p o P .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

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  2 . 3 n o it c e ll o C a t a D .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

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  2 . 4 s i s y l a n A a t a D .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  3

  2 . D k r o w e m a r F h c r a e s e R .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  6

  1 . 5 n o it a c if i s s a l C s ’ n e n o p o K .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  1 . 4 i u g n i L t u p t u O w a R f o n o it a u l a v E c it s .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  I T .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . i E G A P L A

  5 W E

  V O R P P A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . ii E G A P E C N A T P E C C A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ii i E G A P O T T O M .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. v S T N E M G D E L W O N K C A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . vi S T N E T N O C F O E L B A T

  .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . i v i S E L B A T F O T S

  I L .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . x i T C A R T S B A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . x K A R T S B A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. xi N O

  I T C U D O R T N

  I I R E T P A H C . A y d u t S e h t f o d n u o r g k c a B .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  1 . B n o it a l u m r o F m e l b o r P .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  4 . C y d u t S e h t f o s e v it c e j b O .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  4 . D y d u t S e h t f o s ti f e n e B .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  4 . E s m r e T f o n o it i n if e D .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  I V E R L A C

  2

  I T E R O E H T

   I I R E T P A H C . A s e i d u t S d e t a l e R f o w e i v e R .

  1 o o G f o s i s y l a n A r o r r E T t x e n a i s e n o d n I g n it a l s n a r T e t a l s n a r T e l g

  T t x e n a m r e G o t n i .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  7 .

  2 T o t n i d e t a l s n a r s e v it a r e p m I h s il g n E f o s i s y l a n A n o it a l s n a r T e h T T e t a l s n a r e l g o o G y b n a i s e n o d n

  I .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  8 . B s e ir o e h T d e t a l e R f o w e i v e R .

1 n o it a l s n a r T .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  9 . 2 n o it a l s n a r T e n i h c a M .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . 1

  1 . 3 r T e n i h c a M f o n o it a u l a v E n o it a l s n a .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

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  I D N E P P A .

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  88 .

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  V R E T P A H C .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

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  V I R E T P A H C . A e t a l s n a r T e l g o o G n i s r o r r E .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  7

  2 . 1 t p e c n o C d e tt i m O .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  8

  2 . 2 t p e c n o C d e d d A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  3

  3 . 3 t p e c n o C d e t a l s n a rt n U .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . 4

  3 . 4 t p e c n o C d e t a l s n a rt s i M .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  3 . 5 t p e c n o C d e t u ti t s b u S .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0

  7

  5 . 6 il p x E i c t p e c n o C d e t a t .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . 0

  5 . 7 g n i d n i F f o t l u s e R .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  51 . B s r o r r E e c u d e R o t s d o h t e M f o s n o it s e g g u S .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  51 . 1 m r o F d e t a l o s I n i d e t a l s n a r T .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  52 . a e s a C r e w o L n i e p y T .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  53

. b n o it a z ir o g e t a C c it c a t n y S .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  54 . c c r a e S e c n e l a v i u q E e t a ir p o r p p A e h t h .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  55 . d y T ” r e t n E “ t u o h ti w e s a r h P e h t e p .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  56 . 2 h T t a n i b m o C e s d o h t e M e m o S f o n o i .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  I D N E P P A D t n e m u c o t n e m e e r ) .. .. .. .. .. .. . 1 2

  

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  5 . T 4 e l b a n o it a c il p p A f o t n e m e v e i h c A .

  59 T l b a n o it a n i b m o C . 8 .

4 e s d o h t e M f o .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  59 E n o it i d t x e T . 7 . T 4 e l b a .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  58 T a ” r e t n E “ t u o h ti w e p y T . 6 . 4 e l b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  57 E t m o r f s e c n e l a v i u q e h t g n i h c r a e S . 5 . T 4 e l b a L t s i e h .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  56 T e l b a n o it a z ir o g e t a C c it c a t n y S . 4 .

  3 . T 4 e l b a .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . 54 C e s a r e w o L n i e p y T .

  3

  I L S E R U T C

  C y r o g e t a r o r r E h c a E m o r f n o it .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  25 a ir a V s r o r r E . 1 . T 4 e l b a

  23 T b a t p e c n o C d e t a l s n a rt s i M . 3 . 3 e l .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

  22 E a d h c a e n i s r o r r . 2 . T 3 e l b a m u t .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .

  1 N 1 . o a t a D . 1 . T 3 e l b a .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

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  2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

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  1 2 ) . G o o g l e T r a n s l a t e A s s e s s m e n t W ti h E r r o r

A n a l y s i s : A n A tt e m p t T o R e d u c e E r r o r s . Y o g y a k a tr a : D e p a tr m e n t o f E n g il s h

L e tt e r s , F a c u tl y o f L e tt e r s , S a n a t a D h a r m a U n i v e r s ti y ,

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  1 2 . T h e m a i n a s p e c t o f rt a n s l a it o n i s h o w o n e e x p r e s s i o n i n o n e l a n g u a g e i s

r e p l a c e d w ti h e q u i v a l e n t r e p r e s e n t a it o n i n a n o t h e r l a n g u a g e . T h e r e p r e s e n t a it o n

s h o u l d b e e q u i v a l e n t i n t e r m o f s e m a n it c a n d s t y il s it c . G o o g l e T r a n s l a t e a s o n e

f e a t u r e o f rt a n s l a it n g t o o l p r o v i d e d b y G o o g l e a l s o rt y t o f a c li ti a t e t h a t p r o c e s s .

  

H o w e v e ,r t h e r e s e a r c h e r if n d s e r r o r s r e l a t e d t o e q u i a l e n c e i n t h e t h r e e t e x t – v s a n

i P a d u s e r g u i d e , a N a it o n a l G e o g r a p h i c a r it c l e , a n d O w n e r s h i p A g r e e m e n t

• – d o c u m e n t w h i c h a r e rt a n s l a t e d b y G o o g l e T r a n s l a t e . B a s e d o n t h a t i s s u e , t h e r e s e a r c h e r w a n t s t o i n v e s it g a t e f u tr h e r i n t h i s s t u d y .

  I n t h e s t u d y , t h e r e s e a r c h e r a n a l y z e s t w o p r o b l e m s . T h e f ri s t i s if n d i n g

w h a t e r r o r s e x i s t i n t h e r e s u l t o f rt a n s l a it o n a n d t h e s e c o n d i s t h e s u g g e s it n g e f f o r t

t o r e d u c e a l l e r r o r s f o u n d .

  T h e t h r e e t e x t s a r e rt a n s l a t e d b y G o o g l e T r a n s l a t e . T h e n t h e r e s e a r c h e r

c o u n t s t h e e r r o r s b a s e d o n K o p o n e n ’ s e r r o r s c a t e g o ir e s w h i c h d i v i d e e r r o r s i n t w o

b i g c l a s s e s , .i e . r e l a it o n s b e t w e e n s o u r c e a n d t a r g e t c o n c e p t a n d r e l a it o n s b e t w e e n

c o n c e p t s . S - i n c e ti i s it m e il m ti e d t h e r e s e a r c h e r o n l y u s e s t h e f ri s t c a t e g o r y w h i c h

c o v e r s s i x s u b c l a s s e s : a d d e d c o n c e p t , o m i tt e d c o n c e p t , u n rt a n s l a t e d c o n c e p t ,

m i s rt a n s l a t e d c o n c e p t , s u b s t ti u t e d c o n c e p t , a n d e x p il c i t a t e d c o n c e p t . A tf e r t h e

e r r o r s a r e c a t e g o ir z e d , t h e r e s e a r c h e r u s e s s e m a n it c a p p r o a c h t o a n a l y z e a l l e r r o r s

a n d F a r r ú s s u g g e s it o n s t o a tt e m p t r e d u c i n g e r r o r s . T ir a l a n d e r r o r m e t h o d i s a l s o

a p p il e d s i n c e t h i s s t u d y i s d e s c ir p it v e a n d e x p l o r a t o r y r e s e a r c h r e l a t e d t o

t e c h n o l o g y w h i c h i s v e r y o p e n f o r a n y p o s s i b li ti y .

  I n t h e if n a l r e s u tl , t h e r e s e a r c h e r g e t s

  2 6 e r r o r s i n t h e t h r e e t e x t s : 5 e r r o r s i n a d d e d c o n c e p t , 1 8 e r r o r s i n o m i tt e d c o n c e p t ,

  3 2 e r r o r s i n u n rt a n s l a t e d c o n c e p t ,

  1

  3 6 e r r o r s i n m i s rt a n s l a t e d c o n c e p t , 1 e r r o r i n e x p il c ti a t e d c o n c e p t , a n d

  1 4 e r r o r s

i n e x c e p it o n . T h e n t h e r e s e a r c h e r s u g g e s t s t h r e e m e t h o d s t o r e d u c e e r r o r s , .i e .

t p y g i n i n i s o l a t e d f o r m , t e x t e d i it o n , a n d c o m b i n e d m e t h o d s . F r o m t h e t e s it n g t h e

f ri s t m e t h o d c a n r e d u c e a l m o s t

  5 % e r r o r s f o r o m i tt e d , u n rt a n s l a t e d , a n d

m i s rt a n s l a t e d c o n c e p ,t t h e s e c o n d m e t h o d r e d u c e s e r r o r s r e l a t e d t o o tr h o g r a p h i c

e r r o r s a n d l ti e r a l rt a n s l a it o n , a n d t h e t h ri d c a n r e d u c e f o r a l l c a t e g o ir e s .

x

• k u j n u t e p

  , n a u j u t e s r e p n e m u k o d n a d c i h p a r g o e G l a n o it a N l e k it r a , d a P i a n u g g n e p

  n a k il i m e p e k -
• g n a y n a h a l a s e k . n a k u m e ti d K i s k e t a g it e k h a m e jr e ti d u t

  . p e s n o k a r a t u t k a w t a g n i g n e M

, p u k a c n e m g n a y a m a tr e p i r o g e t a k n a k a n u g g n e m a y n a h s il u n e p s a t a b r e t g n a y

, t p e c n o c d e t a l s n a r ts i m , t p e c n o c d e t a l s n a r t n u , t p e c n o c d e tt i m o , t p e c n o c d e d d a

, t p e c n o c d e t u ti t s b s u s d n a t p e c n o c d e t a ti ti c il p x e

  D i r a . n a h a l a s e k i g n a r u g n e m k u t n u e d o t e m a g it n a k n a r a y n e m s il u n e p n a i d u m e K , i g n a r u g n e m t a p a d a m a tr e p e d o t e m a b o c i j u n a h a l a s e k , t p e c n o c d e tt i m o

  4 1 a d a p n o it p e c x e .

  3 1 , a d a p d e t a l s n a r t s i m t p e c n o c , n a h a l a s e k 1 a d a p t p e c n o c d e t a ti c il p x e n a d , n a h a l a s e k

  6

  2 3 a d a p t p e c n o c d e t a l s n a rt n u n a h a l a s e k

  8 1 a d a p t p e c n o c d e tt i m o , n a h a l a s e k

  2 s k e t a g it e k : n a h a l a s e k 5 a d a p t p e c n o c d e d d a , n a h a l a s e k

  6

  D m a l a , l a t o t h a l m u j n a k t a p a d n e m s il u n e p ri h k a l i s a h i r a d n a h a l a s e k

  ú e d o t e m i r a c n e m a b o c n e m k u t n u s r r a F i r a d n a r a s n a h a l a s e k i g n a r u g n e m - P e d o t e m n a k p a r e n e m a g u j s il u n e . t u b e s r e t n a h a l a s e k l a i r t r o r r e d n a r k s e d t a fi s r e b i n i i d u t s a n e r a k n a g n u b u h r e b g n a y f it a r o l p s k e n a d f it p i n i k g n u m h a l a d a a r a c a l a g e s a n a m i d i g o l o n k e t n a g n e d

  S n a h a l a s e k a u m e s h a l e t e . e k i d n a k a n u g g n e m s il u n e p , n a k k o p m o l n a t a k e d n e p k it n a m e s s i s il a n a g n e m k u t n u n a h a l a s e k a u m e s n a d

  

K a r a t n a n a g n u b u h u ti a y r a s e b s a l e k a u d m a l a d i d a y n i g a b m e m g n a y n e n o p o

n a n a g n u b u h n a d t e g r a t p e s n o k n a d r e b m u s p e s n o k

  i x

K A R T S B A

K

  T e t a l s n a r e l g o o G n a k a n u g g n e m n a . u ti h a l e t e S n a h a l a s e k g n u ti h g n e m s il u n e p - n a h a l a s e k i r o g e t a k n a k r a s a d r e b a d a g n a y n a h a l a s e k

  B n a k r a s a d r e . e t a l s n a r T e l g o o G n a g n e d n a k h a m e jr e ti d g n a y , a y n it il e n e m k u t n u n a n i g n i e k r e b i ti l e n e p t u b e s r e t a n e m o n e f . h u a j h i b e l Y r e p g n a . h a l a s a m a u d s i s il a n a g n e m s il u n e p , i n i i d u t s m a l a d i D a m a t ( r o r r e n a h a l a s e k n a k u m e n e m h a l a d a s a u d e k g n a y n a d n a h a m e jr e t li s a h m a l a d i d ) n a h a l a s e k a u m e s i g n a r u g n e m k u t n u n a r a s n a k ir e b m e m h a l a d a

  

, n a k u m e n e m i ti l e n e p n u m a N . t u b e s r e t s e s o r p i s a ti li s a f m e m k u t n u a h a s u r e b

) r o r r e ( n a h a l a s e k g n a y s k e t a g it m a l a d i d i s n e l a v i u q e n a g n e d n a ti a k r e b

  P l a h m a l a d n e l a v i u q e s u r a h t u b e s r e t n a n a d a . n i a l G e l g o o . a k it s il it s n a d k it n a m e s

G a g u j e l g o o h e l o n a k a i d e s i d g n a y n a k h a m e jr e n e m t a l a r u ti f u t a s h a l a s , e t a l s n a r T

  A i d i s e r p s k e u t a u s a n a m i a g a b h a l a d a n a k h a m e jr e n e m m a l a d a m a t u k e p s

a s a h a b m a l a d i d n e l a v i u q e g n a y n a n a d a p i a y n u p m e m t a p a d a s a h a b u t a u s m a l a d

  1 D 2 , a m r a h a t a n a S s a ti s r e v i n U , a rt s a S s a tl u k a F .

  2

  : a tr a k a y g o Y , s ir g g n I a rt s a S n a s u r u J

  1 2 ( s e s s A e t a l s n a r T e l g o o G s r o r r E h ti W t n e m A : s i s y l a n A . s r o r r E e c u d e R o T t p m e t t A n

  2

  I M A D . . )

  I N E D S U N A

   , t p e c n o c d e t a l s n a r t n u n a d t p e c n o c d e t a l s n a r t s i m , e d o t e m a y n n e s r e p 5 r i p m a h

n a d i f a r g o tr o n a g n e d n a g n u b u h r e b g n a y n a h a l a s e k i g n a r u g n e m t a p a d a u d e k

, e m n a d h a if r a h n a h a m e jr e n e p t g n a r u g n e m t a p a d a g it e k e d o a u m e s a d a n a h a l a s e k i e t a k .i r o g

CHAPTER I INTRODUCTION A. Background of the Study In our current world, there is no more limitation since technology is

  developing rapidly. People are facilitated sufficiently to get everything easily, including information. There is a lot of information accessed by people from media. The media can be written media such as newspapers, books, or magazine, audio-visual media such as television and radio, and online media such as internet.

  In accessing them, however, people still get obstacle in case of language. Therefore the existing technology provides very vast information which is not only from one area/country that means one language but also from many countries with their own languages. Ramis (2006, 1) says that "still, the language barrier is the only obstacle for this vast information to be fully shared by all users". Ramis states that accessing information optimally requires people to be literate in more than one language. It becomes problems for those who only master one or two languages. In this context, translation plays an important role to help people to access and understand the information from other languages. People finally do not have to master many languages because translation has done the job.

  Bell (1997:6) defines translation as the replacement of a representation of a text in one language by a representation of an equivalent text in a second language. Translators should find the closest equivalence of words, sentences,

  1 paragraphs, or a whole text from a Source Language (SL) to a Target Language (TL). Along with the developing technology, translation is also influenced by that development. Currently, translation can be done both manually and automatically.

  Manual translation is fully done by human meanwhile automatic translation is done by computer system which is in practice, with or without human assistance (translated from Nababan, 1999: 134). The latter which we can call as Machine Translation (MT) have been the focus of research in translation since 1950s. From the research, US, Canada, and European countries have developed several systems of MT. The Systems are among others Météo, Systran, Eurotra, Ariane, and Susy (Ramis, 2006: 2).

  Indeed, the systems are not perfect tools to result satisfying translation from one language to another language because of several limitation owned by any kind of machine. In his Ph. D. paper, Gispert reports the Bar-Hillel analysis that Fully Automatic High-Quality Translation (FAHQT) was an unreachable goal and that the enthusiasm of the MT research is up and down. It shows scepticism in viewing the existence of MT in contributing for the development of translation itself. But looking at the facts of great demand of tools translating texts to help people facing interlingua condition, the MT is still continuously improved more and more.

  In this study, the researcher focuses on the error analysis of Machine Translation output because the researcher realizes that it is impossible to analyze all aspects of Machine Translation whose scope spread from linguistic aspect to computer system aspect. By error analysis, it is easier to find which part of the output to repair. Particularly, the researcher chooses Google Translate, the most- often-used Machine Translation. The researcher is going to investigate what errors are found in a text translated by Google Translate. It is because the researcher finds several errors when one text in one language is translated into English by Google Translate and finds that it will translate a text differently at two different period of time. Google Translate is one of Google-search-engine features. Google Translate is a Machine Translation based on Systran system (Research at Google, 2012).

  It is explained that Google Translate is statistically based machine translation developed from Franz Joseph Och research. It can translate one text into more or less fifty languages; one of them is from English to Indonesian and vice versa. One of obvious examples is the translation of iPad user guide from English to Bahasa Indonesia. "Keep all your app subscriptions in one convenient place" is translated to "

  Jauhkan semua langganan aplikasi Anda di satu tempat

yang nyaman ". The original meaning in Source Text shifts when it is translated

  using Google Translate. Instead of "Jauhkan", "Keep" should be translated to "Simpan". As said previously, Machine Translations including Google Translate still have limitation. In this case, it is obviously shown that the translation is not equivalent. The Target Text translated by Google Translate gives totally wrong instruction for Target Readers.

  According to the real example above, the researcher is interested to analyze the errors done by Google Translate in translating an English text into Indonesian. The analysis will focus on the translation of three texts; iPad user guide, National Geographic article, and the Ownership Agreement document. By the result of analysis, the researcher hopes that users can minimize their misuse in using Google Translate to translate one text and can use that Machine Translation appropriately.

B. Problem Formulation

  There are two problems formulated in this research, namely

  1. What errors are found in the three texts of the Ownership Agreement document, a National Geographic article, and iPad user guide which are translated into Indonesian using Google Translate?

  2. What suggestions are proposed to reduce errors in using Google-Translate?

C. Objectives of the Study

  By the problem formulated above, the objectives of this research are to find and analyze errors in the Bahasa Indonesia texts of the Ownership Agreement document, National Geography’s article, and iPad user guide which are translated by Google Translate. And the second is finding suggestions to reduce errors in using Google-Translate.

D. Benefits of the Study

  Theoretically the researcher expects that this research contributes as one of pilot studies which can help those who want to study more about error analysis of Machine Translation and that this finding can be secondary data of errors found in the translation done by Google-Translate.

  Practically, the benefit of this study is not to create absolute solution which can finish all problems since this study only focuses on linguistic analysis in Machine Translation, but at least it is to suggest some ways which hopefully can reduce errors done by Google Translate as a tool to produce raw translation.

E. Definition of Terms

  In order to have the same perceptions and terminologies used frequently in this research, the researcher defines the following terms.

  1. Machine Translation

  The term Machine Translation (MT) 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 assistance (Hutchin, 1992: 3).

  2. Error Analysis in Machine Translation

  Error Analysis in machine translation is counting errors of texts translated by Google Translate with a classification of errors. It is also an index of the amount of work required to correct 'raw' Machine Translation output to a standard considered acceptable as a translation (Hutchin, 1992: 164).

3. Google Translate

  Google Translate is an online machine translation system published by Google which provides automatic translation for more or less 57 languages in the world including Indonesian. (Research at Google, 2012).

  Google Translate is produced with some approach: the computer is fed with billions of words of text, both monolingual text in the target language, and aligned text consisting of examples of human translations between the languages. Then it applies statistical learning techniques to build a translation model. (Research at Google, 2012).

CHAPTER II THEORETICAL REVIEW A. Review of Related Studies

  

1. Review of Error Analysis of Google Translate Translating Indonesian Text

into German Text

  The researcher reviews the study on MT has been done by Iman Santoso. On his paper entitled “Analisis Kebahasaan Hasil Terjemahan Google-Translate Teks Bahasa Indonesia Ke Dalam Bahasa Jerman”, he investigated some errors of Google-Translate in translating Indonesian texts into German texts. In his research, he applied linguistic approach by using four aspects of linguistics, i.e.

  Orthography, Morphology, Syntax, and Semantics. After he classifies the data, it was shown that Google-Translate had errors in those four aspects. The errors are simple ones which actually can be translated well by human translators. It indicates that MT, in this case Google-Translate didn't have sufficient ability yet to translate the texts equivalently.

  According to the data, from the four aspects mentioned the most errors belong to morphological errors. There are 25 morphological errors of 59 total errors. An example of orthographical errors is mis-spelling of proper name. "Gayus" and "Aburizal Bakrie atau Ical" in Indonesian text are translated into "Gaius" and "iCal Bakrie" in German text. An example of morphological errors is an Indonesian word "Pendiri" which is translated as "Gründer". The word is semantically correct but morphologically wrong. It must be "Der Gründer". An example of syntactical errors is misstucturing of the sentence "Buku komiknya sendiri akan beredar di AS mulai akhir Desember dengan harga 6,99 dollar AS".

  By Google Translate it is translated "Comic Buch selbst wird in den USA ab Ende Dezember 2010 mit dem Preis von $ 6,99". The

  verbreitet werden

  translation contains errors in passiving, the appropriate translation must be "Das Comicheft wird dann in den USA ab Dezember-Ende 2010 mit dem Preis von $ 6,99 verkauft". In semantic, errors are found in translating the phrase "naik daun" to be "stieg". "naik daun" is a metaphor which is synonymous with "being famous". The appropriate translation must be "berühmt werden.

  Based on his study Santoso concludes that Google-Translate translated texts from Indonesian into German word by word by ignoring its context. Users have to edit the text after translated by Google-Translate in order to get better result. He also highlighted that the shorter the texts, the better the result.

  

2. Review of The Translation Analysis of English Imperatives Translated

into Indonesian by Google Translate

  The analysis of Google-Translate errors also done by Kemala Meilinda Putri. She analyzed the errors of Google-Translate in translating English imperatives into Indonesian.

  She finds that Google-Translate did two categories of errors, those are function word and miss-selection of words with similar meaning Based on the data analyze, the writer conclude that there are 53 errors (43.44%) in function words and 47 errors (38.53%) in miss-selection of words with similar meaning. And there are 22 shifts (18.03%) in structure- shifts. (Putri, 2010; 8)

  Putri states that Google-Translate cannot identify different markers of English and Indonesian imperatives. Similar to Santoso, she said that the machine merely translated the sentence word for word.

  In her suggestions, she asked users to concentrate and to read the result again.

  According to the two previous study about error analysis on machine translation, currently the researcher is going to investigate the errrors on machine translation and tries to give suggestions to users to reduce the errors. This research does not only concern to what errors occured, like the previous studies, but also gives some alternatives to overcome the problem.

B. Review of Related Theories

1. Translation

  There are many definitions of translation. Every expert certainly has his/her own definition based on his/her own perspectives. Bell defined the translation based on some resources as.

  [...] the expression in another language (or target language) of what has been expressed in another, source language, preserving semantic and stylistic equivalences [...] Translation is the replacement of a representation of a text in one language by a representation of an equivalent text in a second language. (1997: 5-6) An expression in one language should have its translated-to-target- language expression. The expression then should be equivalent in term of semantic

  (meaning) and stylistic (the style of language). It is apparent that the keyword “equivalence” is very important. An equivalent text requires the translation to be as similar as possible in case of delivering message in the source language (SL) to the target language (TL) reader. Bell also adds a quite understandable definition for “equivalence”.

  Texts in different languages can be equivalent in different degrees (fully or partially equivalent, in respect of different levels of presentation (equivalent in respect of context, of semantics, of grammar, of lexis, etc.) and at different ranks (word-for-word, phrase-for-phrase, sentence-for- sentence) (1997: 6). The equivalence which is varied shows that it is apparent, and has been for a very long time indeed, that the ideal of total equivalence is impossible to achieve

  (Bell, 1997: 6). Therefore there will be no absolute equivalence which can convey meaning to the TL as similar as in the SL. But at least, it is sufficient to look for the closest equivalence for each word, phrase, and sentence in the TL.

  The following picture is the transformation of a source language text into a target language text. The process takes place in the memory where it analyzes the source language text into a universal semantic representation and synthesizes the semantic representation into the target language text. The following page shows the map of the process of translation.

  Memory Source Analysis Language Text

Semantic

  Representation Target

Synthesis

Language Text

Picture 2.1. Process of Translation

2. Machine Translation

  Hutchin (1992: 1) explains that Machine Translation is a traditional call for programs which can produce ‘raw’ translations of texts in relatively well-defined subject domains, which can be revised to give good quality translated texts at. It is a tool which can be used to translate automatically one text to another language, with or without human assistance.

  There are some system designs for Machine Translation. The first is the direct translation approach: the Machine Translation system is designed in all details specifically for one particular pair of languages in one direction. The second is the interlingua approach, which assumes the possibility of converting texts to and from “meaning” representations common to more than one language. Translation is thus in two stages: from the source language to the interlingua, and from the interlingua into the target language. The third type is the less ambitious transfer approach. Rather than operating in two stages through a single interlingua meaning representation, there are three stages involving, usually, syntactic representations for both source and target texts. The first stage converts texts into intermediate representations in which ambiguities have been resolved irrespective of any other language. In the second stage these are converted into equivalent representations of the target language; and in the third stage, the final target texts are generated.