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L
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C
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R
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Su
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Facu
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E
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I
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m
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Un
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s
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T
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I
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r
ay
m
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.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
I
n
d
o
n
esia
’
s
ca
p
ital
r
elo
ca
tio
n
f
r
o
m
J
ak
ar
ta
to
E
ast
Kalim
an
tan
is
a
s
tr
ateg
ic
p
o
licy
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n
o
u
n
ce
d
b
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Pre
s
id
en
t
J
o
k
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W
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Ap
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2
6
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2
0
1
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a
n
d
f
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alize
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o
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g
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L
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Nu
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b
e
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3
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f
2
0
2
2
r
eg
a
r
d
in
g
th
e
Natio
n
al
C
ap
ital
[
1
]
.
T
h
i
s
d
ec
is
io
n
ad
d
r
ess
es
v
ar
io
u
s
ch
allen
g
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f
ac
in
g
J
ak
ar
ta,
s
u
ch
as
o
v
er
c
r
o
w
d
in
g
th
at
ca
u
s
es
tr
af
f
ic
co
n
g
esti
o
n
,
air
p
o
llu
tio
n
,
an
d
a
clea
n
w
ater
cr
is
is
[
2
]
.
T
h
e
is
lan
d
o
f
J
av
a
h
o
u
s
es
5
6
.
1
%
o
f
I
n
d
o
n
esia
’
s
to
tal
p
o
p
u
latio
n
[
3
]
,
th
u
s
co
n
tr
ib
u
tin
g
5
6
.
5
5
%
to
th
e
n
atio
n
al
g
r
o
s
s
d
o
m
e
s
tic
p
r
o
d
u
ct
(
GDP)
[
4
]
.
T
h
is
f
ac
t
s
h
o
ws
th
e
in
eq
u
ality
o
f
d
ev
el
o
p
m
e
n
t
b
etwe
en
r
eg
io
n
s
in
I
n
d
o
n
esia.
T
h
e
m
o
v
em
en
t
o
f
t
h
e
ca
p
ital
city
to
E
ast
Kalim
an
tan
,
co
v
er
in
g
th
e
ar
ea
s
o
f
P
en
ajam
Pas
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Utar
a
an
d
Ku
t
ai
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tan
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ar
a,
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ex
p
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ted
to
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ed
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ce
th
e
b
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r
d
en
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J
ak
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ta
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d
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c
o
u
r
a
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m
o
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eq
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itab
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n
o
m
ic
g
r
o
wth
th
r
o
u
g
h
o
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t
I
n
d
o
n
esia
[
5
]
.
Ho
wev
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,
th
e
d
ev
elo
p
m
en
t
an
d
r
elo
ca
tio
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o
f
th
e
C
ap
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c
ity
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f
th
e
Nu
s
an
tar
a
(
I
KN)
led
to
v
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u
s
o
p
in
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s
in
th
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co
m
m
u
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ity
,
b
o
th
p
r
o
s
an
d
c
o
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s
.
So
m
e
s
ee
th
is
as
a
s
tr
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ic
s
tep
to
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ed
u
ce
p
o
p
u
latio
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d
en
s
ity
in
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ak
ar
ta
a
n
d
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n
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ag
e
e
q
u
i
tab
le
ec
o
n
o
m
ic
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wth
[
6
]
.
H
o
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t
h
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a
r
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ce
r
n
s
a
b
o
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t
en
v
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o
n
m
en
tal
d
am
ag
e
i
n
Kalim
an
tan
[
7
]
.
Ad
d
itio
n
ally
,
g
o
v
er
n
m
e
n
t
esti
m
ates
s
u
g
g
est
th
at
r
elo
ca
tin
g
t
h
e
ca
p
ital
city
will
co
s
t
R
p
4
6
6
tr
illi
o
n
[
8
]
,
a
lar
g
e
b
u
d
g
et
m
an
y
co
n
s
id
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c
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atter
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ca
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s
id
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r
th
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p
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s
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d
co
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s
r
elate
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to
th
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d
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p
m
e
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t
o
f
I
KN
wh
en
f
o
r
m
u
latin
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f
u
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th
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p
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licies
an
d
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in
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ex
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co
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ce
r
n
s
.
I
n
th
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p
to
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p
r
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tial
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
o
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J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
N
u
s
a
n
ta
r
a
ca
p
ita
l c
ity
s
en
timen
t a
n
a
lysi
s
u
s
in
g
s
u
p
p
o
r
t v
ec
to
r
ma
ch
in
e
…
(
V
a
le
n
cia
E
u
r
e
lia
A
n
g
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Ta
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ia
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1709
elec
tio
n
s
,
d
is
cu
s
s
io
n
s
ab
o
u
t
r
elo
ca
tin
g
a
n
d
d
ev
elo
p
i
n
g
t
h
e
ca
p
ital
city
i
n
cr
ea
s
ed
o
n
s
o
cial
m
ed
ia
[
9
]
.
W
ith
1
4
.
8
m
illi
o
n
ac
tiv
e
X
u
s
er
s
in
I
n
d
o
n
esia
[
1
0
]
,
T
witter
is
n
o
w
X,
a
m
ed
iu
m
f
o
r
p
eo
p
le
to
ex
p
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ess
th
eir
o
p
in
io
n
s
,
a
n
d
it o
f
ten
f
u
n
ctio
n
s
as a
d
ata
s
o
u
r
ce
f
o
r
p
u
b
lic
o
p
in
io
n
a
n
aly
s
is
[
1
1
]
.
T
h
e
alg
o
r
ith
m
s
u
s
ed
to
an
aly
ze
s
en
tim
en
t
in
th
is
r
esear
ch
ar
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
.
T
h
e
s
elec
tio
n
o
f
SVM
is
d
u
e
to
its
e
f
f
ec
tiv
e
h
an
d
lin
g
o
f
d
ata
-
r
ich
en
v
ir
o
n
m
en
ts
s
u
ch
as
tex
t
an
d
its
ab
ilit
y
to
m
an
ag
e
d
if
f
er
e
n
t
d
ata
ty
p
es
th
r
o
u
g
h
v
ar
io
u
s
k
er
n
el
f
u
n
ctio
n
s
[
1
2
]
.
L
R
was
s
elec
ted
b
ec
au
s
e
it
ca
n
g
en
er
ate
p
r
o
b
ab
ilit
ies
th
at
in
d
icate
s
en
tim
e
n
t
ca
teg
o
r
ies
an
d
co
ef
f
icie
n
ts
th
at
ass
e
s
s
f
ea
tu
r
e
im
p
o
r
tan
ce
with
in
th
e
m
o
d
el
[
1
3
]
.
Pre
v
i
o
u
s
r
esear
ch
s
h
o
ws
th
at
SVM
i
s
h
ig
h
ly
ef
f
ec
tiv
e
in
twee
t
s
en
tim
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t
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aly
s
is
,
co
n
s
is
ten
tly
ac
h
iev
in
g
ac
cu
r
ac
ies
ab
o
v
e
8
0
%
[
1
4
]
.
A
n
o
th
er
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
SVM
o
u
tp
er
f
o
r
m
s
L
R
in
s
en
tim
en
t
an
aly
s
is
[
1
5
]
.
Ho
wev
e
r
,
s
o
m
e
p
r
ef
e
r
L
R
f
o
r
tex
t
class
if
icatio
n
,
as
f
in
d
in
g
s
s
u
g
g
est
th
at
L
R
s
u
r
p
ass
es
S
VM
[
1
3
]
.
Fu
r
th
er
m
o
r
e,
r
esear
ch
[
1
6
]
class
if
ied
p
u
b
lic
s
en
t
im
en
t
u
s
in
g
L
R
,
Naïv
e
B
ay
es
,
s
u
p
p
o
r
t
v
ec
to
r
class
if
ier
,
an
d
s
to
ch
asti
c
g
r
ad
ien
t
d
escen
t
,
r
ev
ea
lin
g
t
h
a
t
L
R
p
r
o
d
u
ce
d
th
e
h
ig
h
est
ac
cu
r
ac
y
o
f
8
1
%.
B
y
u
s
in
g
f
ea
tu
r
e
e
x
tr
ac
tio
n
,
th
e
ac
cu
r
ac
y
o
f
l
o
g
is
tic
r
eg
r
ess
io
n
ca
n
ev
e
n
r
ea
ch
ar
o
u
n
d
8
7
%
[
1
7
]
.
T
h
er
ef
o
r
e,
th
is
s
tu
d
y
aim
s
to
an
aly
ze
th
e
ac
cu
r
ac
y
o
f
th
e
SVM
an
d
L
R
alg
o
r
ith
m
s
in
th
e
s
en
tim
en
t
an
aly
s
is
o
f
I
KN
d
e
v
elo
p
m
en
t.
p
u
b
lic
s
en
tim
en
t
r
elate
d
to
th
e
d
ev
elo
p
m
en
t
o
f
I
KN
is
ex
am
i
n
ed
,
in
clu
d
in
g
th
e
I
KN
p
r
esid
en
tia
l
p
alac
e
,
I
KN
to
ll
r
o
ad
,
an
d
I
KN
g
o
v
er
n
m
en
t
o
f
f
ices
,
an
d
i
t
also
co
m
p
ar
es
th
e
ac
cu
r
ac
y
o
f
SVM
an
d
L
R
alg
o
r
ith
m
s
.
Sev
er
al
s
ig
n
if
ica
n
t
in
s
ig
h
ts
in
to
p
u
b
lic
s
en
ti
m
en
t
r
eg
a
r
d
in
g
th
e
d
ev
elo
p
m
e
n
t
o
f
I
b
u
Ko
ta
Nu
s
an
tar
a
is
p
r
o
v
i
d
ed
.
T
h
e
f
in
d
in
g
s
o
n
th
e
m
o
s
t
f
r
eq
u
en
tly
m
e
n
tio
n
ed
wo
r
d
s
h
elp
id
en
tify
th
e
m
ai
n
is
s
u
es
d
is
cu
s
s
ed
b
y
th
e
p
u
b
lic,
th
er
eb
y
o
f
f
er
in
g
v
al
u
ab
le
in
p
u
t
f
o
r
th
e
g
o
v
er
n
m
e
n
t
’
s
f
u
tu
r
e
co
m
m
u
n
icatio
n
s
tr
ateg
ies,
ev
alu
ate
ex
is
tin
g
p
o
licies,
an
d
m
ak
e
m
o
r
e
in
f
o
r
m
ed
d
ec
is
io
n
s
b
ased
o
n
p
u
b
lic
f
ee
d
b
ac
k
.
2.
M
E
T
H
O
D
T
h
is
r
esear
ch
aim
s
to
an
aly
ze
an
d
co
m
p
a
r
e
th
e
ef
f
ec
tiv
en
ess
o
f
b
o
th
SVM
an
d
L
R
to
d
eter
m
in
e
p
o
s
itiv
e
an
d
n
e
g
ativ
e
s
en
tim
e
n
ts
r
elate
d
t
o
d
ev
elo
p
i
n
g
t
h
e
C
ap
ital
c
ity
o
f
Nu
s
an
tar
a
(
I
K
N)
.
Fig
u
r
e
1
s
h
o
ws
th
is
r
esear
ch
wo
r
k
f
lo
w
ap
p
lies
u
s
in
g
th
e
cr
o
s
s
-
in
d
u
s
tr
y
s
tan
d
ar
d
p
r
o
ce
s
s
f
o
r
d
ata
m
in
in
g
(
C
R
I
SP
-
DM
)
ap
p
r
o
ac
h
.
Fig
u
r
e
1
.
R
esear
ch
wo
r
k
f
lo
w
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
1
7
0
8
-
1
7
2
1
1710
2
.
1
.
B
us
ines
s
un
der
s
t
a
nd
ing
B
u
s
in
ess
u
n
d
er
s
tan
d
in
g
f
o
cu
s
es
o
n
u
n
d
er
s
tan
d
in
g
th
e
b
u
s
in
ess
p
r
o
b
lem
an
d
th
e
p
u
r
p
o
s
e
o
f
th
e
d
ata
m
in
in
g
p
r
o
ject.
T
h
is
r
esear
c
h
will
an
aly
ze
p
u
b
lic
s
en
tim
en
t
to
war
d
s
th
e
o
n
g
o
i
n
g
d
ev
elo
p
m
en
t
o
f
I
KN
Nu
s
an
tar
a,
esp
ec
ially
th
e
c
o
n
s
tr
u
ctio
n
o
f
th
e
p
r
esid
en
tia
l
p
alac
e,
to
ll
r
o
ad
s
,
an
d
g
o
v
er
n
m
en
t
o
f
f
ices.
I
n
ad
d
itio
n
,
th
is
r
esear
ch
ai
m
s
to
id
en
tify
in
f
lu
en
tial
a
cc
o
u
n
ts
th
at
f
o
r
m
p
u
b
lic
o
p
in
io
n
a
n
d
s
p
r
ea
d
in
f
o
r
m
atio
n
r
elate
d
to
th
e
co
n
s
tr
u
ctio
n
o
f
I
KN.
2
.
2
.
Da
t
a
un
dersta
nd
ing
Data
u
n
d
er
s
tan
d
in
g
in
v
o
lv
e
s
d
ata
co
ll
ec
tio
n
,
lab
elin
g
,
an
d
ex
p
lo
r
atio
n
to
u
n
d
er
s
tan
d
d
ata
ch
ar
ac
ter
is
tics
an
d
q
u
ality
.
Data
co
llectio
n
is
p
er
f
o
r
m
e
d
u
s
in
g
No
d
eXL
,
wh
ile
d
at
a
lab
elin
g
is
d
o
n
e
m
an
u
ally
.
I
n
t
h
is
r
esear
ch
,
th
e
d
ataset
u
s
ed
is
d
ata
f
r
o
m
p
u
b
lic
twee
ts
r
elev
an
t
to
th
e
I
KN
Nu
s
an
tar
a
d
ev
elo
p
m
e
n
t.
Data
was
co
llected
f
r
o
m
No
v
em
b
er
1
,
2
0
2
3
,
t
o
J
an
u
ar
y
3
1
,
2
0
2
4
,
with
th
e
k
ey
wo
r
d
s
“
P
emb
a
n
g
u
n
a
n
I
K
N
”
(
Dev
e
lo
p
m
en
t
o
f
I
KN)
,
“
I
s
ta
n
a
K
ep
r
esid
en
a
n
I
K
N
”
(
I
KN
p
r
esid
en
tial
p
alac
e
)
,
“
Ja
la
n
To
l
I
K
N
”
(
I
KN
to
ll
r
o
ad
)
,
an
d
“
P
erka
n
to
r
a
n
P
eme
r
in
ta
h
I
K
N
”
(
IK
N
g
o
v
er
n
m
e
n
t
o
f
f
ice
)
.
Data
co
n
tain
s
in
f
o
r
m
atio
n
a
b
o
u
t
in
ter
ac
tio
n
s
b
etwe
en
X
u
s
er
s
,
in
clu
d
in
g
wh
o
in
itiated
th
e
in
ter
ac
tio
n
,
th
e
tar
g
et
o
f
th
e
in
ter
ac
tio
n
,
ty
p
es o
f
in
ter
ac
tio
n
,
d
ates a
n
d
tim
es,
an
d
th
e
c
o
n
ten
t o
f
th
e
tw
ee
ts
.
2
.
3
.
Da
t
a
prepa
ra
t
i
o
n
E
v
er
y
s
tep
is
n
ec
ess
ar
y
to
m
ak
e
th
e
f
in
al
d
ata
s
et
f
o
r
th
e
m
o
d
elin
g
p
h
ases
co
v
er
ed
in
d
ata
p
r
ep
ar
atio
n
.
At
th
is
s
tag
e,
d
ata
p
r
ep
ar
atio
n
in
cl
u
d
es
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
s
p
litt
in
g
in
to
tr
ain
in
g
an
d
test
in
g
d
ata,
an
d
f
ea
tu
r
e
e
x
tr
ac
tio
n
.
2
.
3
.
1
.
Da
t
a
prepro
ce
s
s
ing
Data
p
r
ep
r
o
ce
s
s
in
g
i
n
v
o
lv
es
tr
an
s
f
o
r
m
in
g
an
d
s
elec
tin
g
d
ata
to
m
a
k
e
it
m
o
r
e
s
tr
u
ctu
r
ed
a
n
d
u
n
d
er
s
tan
d
a
b
le.
T
h
e
m
ai
n
g
o
al
is
to
o
p
tim
ize
tex
t
d
ata
to
b
e
an
aly
ze
d
ef
f
ec
tiv
ely
in
th
e
s
en
tim
en
t
class
if
icatio
n
p
r
o
ce
s
s
[
1
8
]
.
Da
ta
p
r
ep
r
o
ce
s
s
in
g
co
n
s
is
ts
o
f
:
a)
C
ase
f
o
ld
in
g
: tr
an
s
f
o
r
m
s
all
te
x
ts
in
to
lo
wer
ca
s
e.
b)
Data
c
lean
in
g
:
r
em
o
v
es
u
n
n
ec
ess
ar
y
elem
en
ts
f
r
o
m
th
e
tex
t,
s
u
ch
as
s
p
ec
ial
o
r
n
o
n
-
alp
h
ab
etic
ch
ar
ac
ter
s
,
s
y
m
b
o
ls
(
!
@
#
$
%^&
*
+_
/ a
n
d
o
th
er
s
)
,
em
o
tico
n
s
,
h
y
p
er
lin
k
s
,
an
d
o
t
h
er
s
.
c)
T
o
k
en
izatio
n
:
b
r
ea
k
s
s
en
ten
ce
s
in
to
ch
u
n
k
s
o
f
wo
r
d
s
ca
lled
t
o
k
en
s
.
d)
No
r
m
aliza
tio
n
:
co
r
r
ec
ts
wr
itin
g
an
d
s
p
ellin
g
er
r
o
r
s
in
th
e
tex
t
an
d
co
n
v
er
ts
ab
b
r
e
v
iatio
n
s
o
r
s
lan
g
in
to
s
tan
d
ar
d
f
o
r
m
s
.
e)
Sto
p
wo
r
d
r
em
o
v
al:
r
em
o
v
es
co
m
m
o
n
wo
r
d
s
th
at
o
f
ten
ap
p
ea
r
in
tex
t
b
u
t
h
a
v
e
n
o
m
ea
n
in
g
an
d
d
o
n
o
t
h
av
e
a
s
ig
n
if
ican
t e
f
f
ec
t.
f)
Stem
m
in
g
:
tr
an
s
f
o
r
m
s
wo
r
d
s
b
y
c
o
n
v
e
r
tin
g
a
f
f
ix
ed
wo
r
d
s
in
to
b
asic
wo
r
d
s
t
h
at
m
atc
h
th
e
lan
g
u
ag
e
s
tr
u
ctu
r
e
u
s
in
g
Sas
tr
awi,
a
f
a
m
o
u
s
I
n
d
o
n
esian
s
tem
m
in
g
lib
r
ar
y
.
T
ab
le
1
d
em
o
n
s
tr
ates
th
e
s
tep
-
by
-
s
tep
p
r
e
p
r
o
ce
s
s
in
g
an
d
its
ch
an
g
es
in
twee
t
d
ata,
s
h
o
win
g
h
o
w
th
e
o
r
ig
in
al
twee
t
is
tr
an
s
f
o
r
m
ed
.
So
m
e
r
o
ws
m
ay
co
n
tain
e
m
p
t
y
lis
ts
af
ter
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
s
u
ch
as
clea
n
in
g
,
to
k
en
izatio
n
,
s
to
p
wo
r
d
r
em
o
v
al,
n
o
r
m
aliza
tio
n
,
an
d
s
tem
m
in
g
.
I
t
is
b
ec
au
s
e
th
e
wo
r
d
s
in
th
o
s
e
r
o
ws
ar
e
d
elete
d
d
u
r
in
g
p
r
ep
r
o
ce
s
s
in
g
.
T
h
er
ef
o
r
e,
r
o
ws
with
em
p
ty
lis
ts
wil
l
b
e
r
em
o
v
e
d
,
an
d
th
e
in
d
ex
will
b
e
r
eo
r
g
a
n
ized
to
m
ai
n
tain
d
ata
c
o
n
s
is
ten
cy
f
o
r
f
u
r
th
e
r
an
aly
s
is
.
2
.
3
.
2
.
Sp
lit
da
t
a
T
h
e
s
p
litt
in
g
d
ata
s
tag
e
i
n
s
en
tim
en
t
an
aly
s
is
b
r
ea
k
s
d
o
wn
th
e
d
ataset
in
to
t
r
ain
in
g
an
d
test
in
g
d
ata.
T
h
is
p
r
o
ce
s
s
is
im
p
o
r
tan
t
in
b
u
ild
in
g
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
in
clu
d
in
g
s
en
tim
en
t
an
aly
s
is
.
T
h
e
m
o
d
el
lear
n
s
f
r
o
m
tr
ain
in
g
d
ata
a
n
d
th
e
n
t
ests
it
s
ac
cu
r
ac
y
u
s
in
g
test
in
g
d
ata
to
e
v
alu
ate
its
p
er
f
o
r
m
an
ce
.
T
h
e
d
ata
will
b
e
d
iv
id
ed
in
to
a
s
p
lit
to
8
0
%
o
f
th
e
d
ata
as
tr
ain
in
g
d
ata
an
d
2
0
%
as
test
in
g
d
ata
.
2
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3
.
3
.
F
ea
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x
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ra
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T
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m
f
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s
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d
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wh
ich
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s
tex
t
m
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ac
cu
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ately
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h
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weig
h
t
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f
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ch
wo
r
d
is
co
m
p
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n
a
te
x
t
[
1
9
]
.
T
h
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s
co
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is
ca
lcu
lated
b
ased
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all
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I
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2
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4
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u
s
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delin
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m
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tag
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p
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ed
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if
y
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ased
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tin
g
d
ata.
I
n
th
is
r
esear
ch
,
th
e
alg
o
r
ith
m
s
u
s
ed
ar
e
SVM
an
d
L
R
.
T
h
is
r
esear
ch
o
p
tim
izes
th
e
SVM
alg
o
r
ith
m
b
y
test
in
g
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e
co
s
t
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C
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p
ar
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alu
e
o
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r
,
R
B
F,
p
o
ly
n
o
m
ial,
an
d
s
ig
m
o
id
k
er
n
els.
T
ab
le
1
.
Data
p
r
ep
r
o
ce
s
s
in
g
r
e
s
u
lts
No
S
t
a
g
e
s
Tw
e
e
t
C
h
a
n
g
e
s
1
O
r
i
g
i
n
a
l
Tw
e
e
t
K
e
p
i
k
i
r
a
n
,
l
a
h
i
y
a
j
u
g
a
y
a
.
t
o
l
k
a
l
t
i
m
d
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b
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n
j
u
g
a
p
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za
m
a
n
p
a
k
j
o
k
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w
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n
m
u
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g
k
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n
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p
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k
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n
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m
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k
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n
sa
m
p
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a
k
h
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rn
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a
d
a
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a
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t
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w
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t
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n
b
y
t
h
e
u
ser.
2
C
a
se
f
o
l
d
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g
a
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d
d
a
t
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c
l
e
a
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k
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K
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p
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k
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mes
k
e
p
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k
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ra
n
(
t
h
i
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k
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g
)
.
A
l
l
p
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t
u
a
t
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s
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p
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a
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a
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a
c
t
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r
s (e.
g
.
,
)
a
r
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e
m
o
v
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d
.
3
To
k
e
n
i
z
a
t
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o
n
[ke
p
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k
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r
a
n
,
l
a
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a
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a
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k
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a
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a
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k
e
n
s.
4
N
o
r
mal
i
z
a
t
i
o
n
[
t
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r
p
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k
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k
a
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y
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p
e
,
a
k
h
i
rn
y
a
,
a
d
a
,
i
k
n
,
w
u
w
]
k
e
p
i
k
i
r
a
n
(
t
h
i
n
k
i
n
g
)
.
b
e
c
o
mes
t
e
rp
i
k
i
r
k
a
n
(
t
h
o
u
g
h
t
o
f
)
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p
a
s
(
j
u
st
i
n
t
i
me)
b
e
c
o
me
sa
a
t
(
w
h
e
n
)
.
5
S
t
o
p
w
o
r
d
r
e
mo
v
a
l
[t
e
r
p
i
k
i
rk
a
n
,
t
o
l
,
k
a
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d
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b
a
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k
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,
t
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k
,
k
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m
a
j
u
a
n
,
p
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m
b
a
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g
u
n
a
n
,
i
k
n
]
Re
m
o
v
i
n
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a
h
(
e
x
p
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a
(
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s)
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a
(
a
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a
(
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a
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t
(
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(
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)
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k
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(
may
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a
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(
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(
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a
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(
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i
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s)
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a
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a
(
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(
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a
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w
(
e
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i
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.
6
S
t
e
mm
i
n
g
[
p
i
k
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r
,
t
o
l
,
k
a
l
t
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m
,
b
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a
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b
a
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,
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k
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]
t
e
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p
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k
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rk
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n
(
t
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t
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f
)
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s
p
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(
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(
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(
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k
e
m
a
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a
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(
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e
ss)
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e
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s
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a
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(
p
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e
ss)
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m
b
a
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a
n
(
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u
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t
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e
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mes
b
a
n
g
u
n
(
b
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i
l
d
)
.
2
.
4
.
1
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
SVM
is
a
p
ar
t
o
f
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
th
at
u
s
e
a
s
u
p
er
v
is
ed
lear
n
in
g
ca
teg
o
r
y
f
o
r
class
if
y
in
g
an
d
r
eg
r
ess
in
g
task
s
.
I
t seek
s
th
e
b
est h
y
p
er
p
la
n
e
b
y
m
ax
im
izin
g
th
e
d
is
tan
ce
b
e
twee
n
d
ata
g
r
o
u
p
s
(
m
ar
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in
)
.
T
h
e
h
y
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p
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f
u
n
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n
s
ep
ar
ates
d
if
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er
e
n
t
g
r
o
u
p
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o
f
d
ata.
T
h
e
m
ar
g
in
is
ap
p
lied
to
s
ep
ar
ate
th
e
d
ata
g
r
o
u
p
s
;
in
t
h
is
ca
s
e,
p
o
s
i
tiv
e
d
ata
(
+
1
)
f
r
o
m
n
eg
ativ
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d
ata
(
-
1
)
m
ak
es
it
m
o
r
e
lik
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at
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o
r
ith
m
ca
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if
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th
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in
to
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co
r
r
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t
g
r
o
u
p
s
m
o
r
e
ac
c
u
r
atel
y
[
2
1
]
.
A
SVM
‘
s
m
ain
a
d
v
an
tag
e
is
its
ab
ilit
y
to
an
aly
ze
v
ar
io
u
s
d
ata
ty
p
es.
I
t
is
f
ac
ilit
ated
b
y
k
er
n
el
f
u
n
ctio
n
s
,
wh
ich
allo
w
SVM
to
o
p
er
ate
in
h
i
g
h
er
d
im
en
s
io
n
al
s
p
ac
es
with
o
u
t
ex
p
licit
ca
lcu
latio
n
s
.
T
h
is
f
e
atu
r
e
en
a
b
les
SVM
to
m
an
a
g
e
n
o
n
-
lin
ea
r
d
ata
ef
f
ec
tiv
ely
.
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k
er
n
el
f
u
n
ctio
n
s
in
clu
d
e
lin
ea
r
,
p
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l
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o
m
i
a
l
,
g
a
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s
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i
a
n
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a
d
i
a
l
b
as
i
s
f
u
n
c
t
i
o
n
(
R
B
F
)
,
a
n
d
s
i
g
m
o
i
d
w
it
h
t
h
e
f
o
l
l
o
wi
n
g
f
u
n
c
t
i
o
n
a
l
e
q
u
a
ti
o
n
s
[
2
2
]
.
a)
L
in
ea
r
Ker
n
el
(
,
)
=
(
1
)
b)
R
B
F
Ker
n
el
(
,
)
=
[
−
‖
−
‖
2
]
,
>
0
(
2
)
c)
Po
ly
n
o
m
ial
Ker
n
el
(
,
)
=
(
1
+
)
(
3
)
d)
Sig
m
o
id
Ker
n
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
38
,
No
.
3
,
J
u
n
e
20
25
:
1
7
0
8
-
1
7
2
1
1712
(
,
)
=
ℎ
(
+
)
(
4
)
W
h
er
e:
:
Featu
r
e
v
ec
to
r
f
r
o
m
t
h
e
tr
ain
in
g
d
ataset.
:
Featu
r
e
v
ec
to
r
f
r
o
m
t
h
e
test
in
g
d
ataset.
:
Scalar
f
ac
to
r
to
ad
j
u
s
t sen
s
itiv
ity
m
o
d
el
to
d
if
f
er
e
n
ce
s
in
f
ea
tu
r
e
s
p
ac
e.
:
Deg
r
ee
o
f
t
h
e
p
o
l
y
n
o
m
ial.
:
C
o
n
s
tan
t te
r
m
.
2
.
4
.
2
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
LR
ap
p
lies
s
u
p
er
v
is
ed
lear
n
in
g
to
class
if
y
in
g
a
d
ep
en
d
en
t
v
ar
iab
le
’
s
p
r
o
b
ab
ilit
y
b
ased
o
n
in
d
ep
en
d
en
t
v
ar
iab
les
[
2
3
]
.
L
R
,
d
ev
el
o
p
ed
f
r
o
m
lin
ea
r
r
eg
r
ess
io
n
,
is
u
s
ed
f
o
r
b
in
ar
y
class
if
icatio
n
,
wh
er
e
th
e
d
e
p
en
d
e
n
t
v
ar
iab
le
is
d
is
cr
ete
(
e.
g
.
,
0
o
r
1
,
y
es
o
r
n
o
)
.
L
R
u
s
es
s
ig
m
o
i
d
o
r
lo
g
is
tic
f
u
n
ctio
n
s
to
co
n
v
er
t
lin
ea
r
in
p
u
ts
in
to
v
alu
es
b
etwe
en
0
an
d
1
,
en
s
u
r
in
g
th
e
p
r
ed
ictio
n
r
esu
lts
ar
e
alwa
y
s
with
in
th
is
r
a
n
g
e
[
2
4
]
.
I
n
s
en
tim
en
t
an
aly
s
is
,
L
R
class
if
ies
tex
ts
,
s
u
ch
as
twee
ts
o
r
r
ev
i
ews,
in
to
p
o
s
itiv
e
o
r
n
eg
ativ
e
s
en
ti
m
en
t
ca
teg
o
r
ies
b
y
r
elatin
g
tex
t f
ea
tu
r
es to
s
en
tim
en
t c
ateg
o
r
ies
[
2
5
]
.
2
.
5
.
E
v
a
lua
t
i
o
n
E
v
alu
atio
n
is
th
e
s
tag
e
o
f
m
o
d
el
p
er
f
o
r
m
an
ce
ass
ess
m
en
t.
At
th
is
s
tag
e,
th
e
m
o
d
el
’
s
s
en
tim
en
t
class
if
icatio
n
is
ass
e
s
s
ed
to
d
eter
m
in
e
th
e
lev
el
o
f
ac
c
u
r
ac
y
p
r
o
d
u
ce
d
b
y
ea
c
h
m
o
d
e
l.
T
h
e
alg
o
r
ith
m
’
s
p
er
f
o
r
m
an
ce
is
ev
alu
ated
u
s
in
g
th
e
co
n
f
u
s
io
n
m
atr
ix
[
2
6
]
.
I
t
in
clu
d
es
f
o
u
r
k
ey
p
a
r
am
eter
s
:
tr
u
e
p
o
s
itiv
e
(
T
P),
f
alse
p
o
s
itiv
e
(
FP
)
,
tr
u
e
n
eg
ativ
e
(
T
N)
,
an
d
f
alse
n
eg
ativ
e
(
FN)
.
T
r
u
e
p
o
s
itiv
e
r
ef
er
s
to
th
e
n
u
m
b
er
o
f
p
o
s
itiv
e
d
ata
ac
cu
r
ately
class
if
ied
.
Fal
s
e
p
o
s
i
tiv
e
r
ef
er
s
to
th
e
n
u
m
b
er
o
f
n
eg
ati
v
e
d
ata
in
co
r
r
ec
tly
class
if
ied
as
p
o
s
itiv
e.
Me
an
wh
ile,
tr
u
e
n
eg
ativ
e
r
ef
er
s
to
th
e
n
u
m
b
e
r
o
f
n
e
g
ativ
e
d
ata
co
r
r
ec
tl
y
class
if
ied
,
wh
ile
f
alse
n
eg
ati
v
e
is
p
o
s
itiv
e
d
ata
in
c
o
r
r
ec
t
ly
p
r
e
d
icted
as
n
eg
ativ
e.
Ac
cu
r
ac
y
,
p
r
ec
is
i
o
n
,
r
ec
all,
an
d
th
e
F1
-
s
co
r
e
ar
e
ca
lcu
lated
f
r
o
m
t
h
ese
v
alu
es,
wh
ich
h
elp
e
x
am
in
e
th
e
cla
s
s
if
icatio
n
m
o
d
el
’
s
ef
f
ec
tiv
en
ess
[
2
6
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
o
v
er
all
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
in
d
icate
th
at
p
u
b
lic
s
en
tim
en
t
to
war
d
s
th
e
I
KN
is
g
en
er
ally
po
s
itiv
e.
B
r
o
ad
p
u
b
lic
s
u
p
p
o
r
t
f
o
r
th
e
d
ev
elo
p
m
en
t
o
f
th
e
I
K
N
is
r
ef
lecte
d
in
th
eir
co
n
f
id
en
ce
in
th
e
p
r
o
ject
’
s
p
r
o
g
r
ess
an
d
co
m
m
itm
en
t
to
it
s
s
u
cc
ess
f
u
l
co
m
p
letio
n
.
T
h
e
p
u
b
lic
also
ap
p
r
ec
iates
th
e
ar
c
h
itectu
r
al
d
esig
n
o
f
th
e
I
KN
p
r
esid
en
tial
p
alac
e
,
esp
ec
ia
lly
th
e
in
teg
r
atio
n
o
f
g
r
ee
n
s
p
ac
es
an
d
I
n
d
o
n
esian
cu
ltu
r
al
elem
en
ts
.
Pu
b
lic
tr
u
s
t
in
th
e
p
r
o
ject
’
s
p
r
o
g
r
ess
an
d
g
o
v
er
n
m
en
t
lead
er
s
h
ip
is
ev
id
en
t,
esp
ec
ially
in
ac
h
iev
in
g
k
e
y
d
ev
elo
p
m
e
n
t
g
o
als.
T
h
e
p
u
b
li
c
is
o
p
tim
is
tic
th
at
th
e
I
KN
t
o
ll
r
o
ad
will
co
n
tr
ib
u
t
e
t
o
o
v
er
all
in
f
r
astru
ctu
r
e
im
p
r
o
v
em
e
n
ts
an
d
ac
h
ie
v
e
n
atio
n
al
d
ev
elo
p
m
en
t
tar
g
ets
ef
f
ec
tiv
ely
.
Po
s
itiv
e
s
en
tim
en
t
to
war
d
s
th
e
I
KN
g
o
v
er
n
m
en
t
b
u
ild
in
g
r
e
f
lects
p
u
b
lic
s
u
p
p
o
r
t
f
o
r
its
ar
ch
itectu
r
al
q
u
ality
,
d
esig
n
,
an
d
v
is
io
n
.
An
o
th
e
r
m
ajo
r
s
tu
d
y
f
in
d
in
g
is
th
at
t
h
e
SVM
alg
o
r
ith
m
co
n
s
is
ten
tly
p
er
f
o
r
m
s
b
etter
th
an
o
th
e
r
m
o
d
els
lik
e
LR
,
with
ac
cu
r
ac
y
r
ates
as
h
ig
h
as
1
0
0
%
f
o
r
th
e
I
KN
p
r
esid
en
tial
p
alac
e
d
ataset
an
d
an
o
v
er
all
a
v
er
ag
e
o
f
9
1
.
9
7
%.
T
h
e
d
is
tr
ib
u
tio
n
o
f
d
ata
af
ter
p
r
ep
r
o
ce
s
s
in
g
an
d
th
e
s
en
tim
en
t c
ateg
o
r
ies m
an
u
ally
lab
eled
ar
e
s
h
o
wn
in
T
ab
le
2
.
Gen
e
r
ally
,
t
h
ey
ex
h
ib
it
m
o
r
e
p
o
s
itiv
e
s
en
tim
en
ts
th
an
n
eg
ativ
e
s
en
tim
en
t
s
f
o
r
all
d
atasets
.
T
h
e
d
e
v
elo
p
m
e
n
t
o
f
I
KN
h
a
s
th
e
h
ig
h
est
n
u
m
b
er
o
f
twee
ts
,
wh
ile
o
n
ly
a
s
m
all
a
m
o
u
n
t
o
f
d
ata
co
n
tain
s
d
is
cu
s
s
io
n
s
ab
o
u
t I
KN
p
r
esid
e
n
t p
alac
e
.
T
ab
le
2
.
Nu
m
b
er
o
f
s
en
tim
en
t
ca
teg
o
r
ies
D
a
t
a
s
e
t
P
o
si
t
i
v
e
N
e
g
a
t
i
v
e
To
t
a
l
Tw
e
e
t
s
D
e
v
e
l
o
p
m
e
n
t
o
f
I
K
N
1
5
,
2
1
4
(
5
3
%)
1
3
,
3
6
4
(
4
7
%)
2
8
,
5
7
8
I
K
N
p
r
e
si
d
e
n
t
i
a
l
p
a
l
a
c
e
3
4
(
6
2
%)
2
1
(
3
8
%)
55
I
K
N
t
o
l
l
r
o
a
d
5
7
7
(
6
1
%)
3
7
5
(
3
9
%)
9
5
2
I
K
N
g
o
v
e
r
n
me
n
t
o
f
f
i
c
e
s
4
7
6
(
5
5
%)
3
9
1
(
4
5
%)
8
6
7
To
t
a
l
1
6
,
3
0
1
(
5
4
%)
1
4
,
1
5
1
(
4
6
%)
3
0
,
4
5
2
3
.
1
.
Dev
el
o
pm
ent
o
f
I
K
N
Fig
u
r
e
2
s
h
o
ws
th
e
wo
r
d
s
f
r
eq
u
en
tly
ap
p
ea
r
in
g
in
twee
ts
ab
o
u
t
th
e
d
ev
el
o
p
m
en
t
o
f
I
KN
in
b
o
th
p
o
s
itiv
e
an
d
n
e
g
ativ
e
s
en
tim
e
n
ts
.
T
h
e
wo
r
d
s
‘
ikn
’
an
d
‘
b
a
n
g
u
n
’
d
o
m
in
ate
b
o
t
h
s
en
tim
e
n
ts
,
in
d
icatin
g
th
at
th
ese
wo
r
d
s
ar
e
th
e
m
ain
to
p
ics
in
d
is
cu
s
s
io
n
s
r
elate
d
to
I
KN
d
ev
elo
p
m
en
t.
Ho
wev
e
r
,
th
eir
f
r
eq
u
en
c
y
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
N
u
s
a
n
ta
r
a
ca
p
ita
l c
ity
s
en
timen
t a
n
a
lysi
s
u
s
in
g
s
u
p
p
o
r
t v
ec
to
r
ma
ch
in
e
…
(
V
a
le
n
cia
E
u
r
e
lia
A
n
g
elie
Ta
n
ia
)
1713
h
ig
h
er
in
t
h
e
p
o
s
itiv
e
co
n
tex
t,
wh
ich
c
o
u
ld
in
d
icate
a
m
o
r
e
r
o
b
u
s
t
r
esp
o
n
s
e
o
r
m
o
r
e
s
u
p
p
o
r
t
f
o
r
th
e
i
d
ea
o
f
I
KN
d
ev
elo
p
m
en
t
.
I
n
p
o
s
itiv
e
s
en
tim
en
t,
th
e
wo
r
d
s
‘
d
u
k
u
n
g
’
(
s
u
p
p
o
r
t)
a
n
d
‘
ko
mitmen
’
(
co
m
m
itm
en
t)
ap
p
ea
r
,
s
h
o
win
g
th
e
p
u
b
lic
’
s
c
o
n
f
id
e
n
ce
an
d
en
co
u
r
ag
em
e
n
t
f
o
r
th
e
co
n
tin
u
atio
n
an
d
s
u
cc
ess
o
f
I
KN
d
ev
elo
p
m
en
t
.
Me
an
wh
ile,
wo
r
d
s
lik
e
‘
r
a
ky
a
t
’
(
p
eo
p
le)
an
d
‘
p
a
s
lo
n
’
(
p
r
esid
en
tial c
an
d
id
ates)
ap
p
ea
r
in
a
n
eg
ativ
e
co
n
tex
t.
T
ab
le
3
d
etails
an
an
a
ly
s
is
o
u
tlin
in
g
ef
f
ec
tiv
e
s
tr
ateg
ies
to
u
tili
ze
th
is
s
en
tim
en
t
d
ata
to
ad
d
r
ess
th
e
p
u
b
lic
’
s
s
tan
ce
o
n
I
KN
d
ev
e
lo
p
m
en
t.
Pu
b
lic
r
esp
o
n
s
es
h
i
g
h
lig
h
ted
s
ev
e
r
al
is
s
u
es
r
elate
d
to
f
u
n
d
in
g
I
KN
p
r
o
jects.
T
h
e
g
o
v
er
n
m
en
t
m
u
s
t
clar
if
y
th
e
allo
ca
tio
n
o
f
th
ese
f
u
n
d
s
an
d
u
s
e
th
em
to
r
esp
o
n
d
t
o
p
u
b
lic
co
n
ce
r
n
s
.
I
t
is
im
p
o
r
tan
t
to
em
p
h
asize
th
at
th
e
d
e
v
elo
p
m
en
t
o
f
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3
P
o
l
y
n
o
m
i
a
l
0
.
7
1
8
0
.
6
9
5
0
.
7
1
3
0
.
7
1
8
0
.
7
1
8
RBF
0
.
7
7
6
0
.
8
1
0
0
.
8
0
5
0
.
8
0
5
0
.
8
0
5
S
i
g
m
o
i
d
0
.
7
7
0
0
.
7
9
9
0
.
8
2
8
0
.
8
0
5
0
.
7
6
4
B
ased
o
n
T
ab
le
8
,
th
e
SVM
a
lg
o
r
ith
m
h
as
h
ig
h
er
ac
cu
r
ac
y
th
an
L
R
o
n
th
e
v
ar
io
u
s
d
atasets
tes
ted
.
On
th
e
I
KN
d
ev
elo
p
m
en
t
d
a
taset,
SVM
ac
h
iev
ed
an
ac
c
u
r
ac
y
o
f
8
9
.
6
8
%,
wh
ile
L
R
r
ec
o
r
d
ed
8
3
.
8
9
%.
Fo
r
th
e
I
KN
p
r
esid
en
tial
p
a
la
ce
d
ataset,
b
o
th
al
g
o
r
ith
m
s
att
ain
ed
an
ac
c
u
r
ac
y
o
f
1
0
0
%.
O
n
th
e
I
KN
to
ll
r
o
ad
d
ataset,
th
e
SVM
ac
cu
r
ac
y
was
9
3
.
7
2
%
wh
ile
L
R
was
8
9
.
5
3
%,
an
d
o
n
th
e
I
KN
g
o
v
er
n
m
en
t
o
f
f
ice
d
ataset
,
SVM
ac
h
iev
ed
8
4
.
4
8
%
wh
ile
L
R
was
8
1
.
0
3
%.
T
h
e
av
er
a
g
e
ac
cu
r
ac
y
f
o
r
SVM
is
9
1
.
9
7
%,
wh
ile
L
R
h
as
8
8
.
6
1
%.
T
ab
le
8
.
C
o
m
p
a
r
is
o
n
o
f
SVM
an
d
L
R
alg
o
r
ith
m
D
a
t
a
s
e
t
A
l
g
o
r
i
t
h
m
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
s
c
o
r
e
D
e
v
e
l
o
p
m
e
n
t
o
f
I
K
N
S
V
M
8
9
.
6
8
%
8
9
.
7
4
%
8
9
.
6
8
%
8
9
.
6
9
%
LR
8
3
.
8
9
%
8
4
.
2
9
%
8
3
.
8
9
%
8
3
.
9
1
%
I
K
N
p
r
e
si
d
e
n
t
i
a
l
p
a
l
a
c
e
S
V
M
1
0
0
.
0
0
%
1
0
0
.
0
0
%
1
0
0
.
0
0
%
1
0
0
.
0
0
%
LR
1
0
0
.
0
0
%
1
0
0
.
0
0
%
1
0
0
.
0
0
%
1
0
0
.
0
0
%
I
K
N
t
o
l
l
r
o
a
d
S
V
M
9
3
.
7
2
%
9
3
.
7
4
%
9
3
.
7
2
%
9
3
.
6
9
%
LR
8
9
.
5
3
%
9
0
.
1
5
%
8
9
.
5
3
%
8
9
.
3
1
%
I
K
N
g
o
v
e
r
n
me
n
t
o
f
f
i
c
e
s
S
V
M
8
4
.
4
8
%
8
4
.
7
7
%
8
4
.
4
8
%
8
4
.
3
9
%
LR
8
1
.
0
3
%
8
1
.
5
9
%
8
1
.
0
3
%
8
0
.
8
4
%
A
v
e
r
a
g
e
S
V
M
9
1
.
9
7
%
9
2
.
0
6
%
9
1
.
9
7
%
9
1
.
9
4
%
LR
8
8
.
6
1
%
8
9
.
0
1
%
8
8
.
6
1
%
8
8
.
5
2
%
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