I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
9
,
No
.
1
,
Ju
ly
2
0
2
5
,
p
p
.
364
~
3
7
3
I
SS
N:
2
5
0
2
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijeecs.v
3
9
.i
1
.
pp
364
-
3
73
364
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
A sentim
ent
a
na
l
y
sis
on skewed p
r
o
duct
revi
ews
:
Be
n & Jerry
's
ice cr
ea
m
Na
bil
la
Nurulita
Dew
i
1
,
Sek
a
r
G
esti
Am
a
lia
Ut
a
m
i
1
,
Sh
a
ls
a
bil
a
Aura
Adia
r
1
,
H
a
s
a
n Dw
i C
a
hy
o
no
2
1
D
e
p
a
r
t
me
n
t
o
f
I
n
f
o
r
mat
i
c
s
,
F
a
c
u
l
t
y
o
f
I
n
f
o
r
mat
i
o
n
T
e
c
h
n
o
l
o
g
y
a
n
d
D
a
t
a
S
c
i
e
n
c
e
,
U
n
i
v
e
r
si
t
a
s Se
b
e
l
a
s
M
a
r
e
t
,
S
u
r
a
k
a
r
t
a
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
o
f
D
a
t
a
S
c
i
e
n
c
e
,
F
a
c
u
l
t
y
o
f
I
n
f
o
r
ma
t
i
o
n
T
e
c
h
n
o
l
o
g
y
a
n
d
D
a
t
a
S
c
i
e
n
c
e
,
U
n
i
v
e
r
s
i
t
a
s Se
b
e
l
a
s
M
a
r
e
t
,
S
u
r
a
k
a
r
t
a
,
I
n
d
o
n
e
s
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
24
,
2
0
2
4
R
ev
is
ed
Dec
2
,
2
0
2
4
Acc
ep
ted
Mar
25
,
2
0
2
5
S
e
n
ti
m
e
n
t
a
n
a
ly
sis o
f
p
ro
d
u
c
t
re
v
iew
s
o
ffe
rs
v
a
lu
a
b
le
i
n
sig
h
ts
i
n
to
c
o
n
su
m
e
r
p
e
rsp
e
c
ti
v
e
s,
wh
ich
c
a
n
in
f
o
r
m
p
ro
d
u
c
t
d
e
v
e
lo
p
m
e
n
t
a
n
d
m
a
rk
e
ti
n
g
stra
teg
ies
.
G
iv
e
n
t
h
e
g
r
o
win
g
i
m
p
o
rtan
c
e
o
f
u
se
r
-
g
e
n
e
ra
ted
c
o
n
ten
t
li
k
e
p
ro
d
u
c
t
re
v
iew
s,
th
is
st
u
d
y
e
x
p
lo
re
d
se
n
t
i
m
e
n
t
c
las
sifica
ti
o
n
in
o
n
li
n
e
re
v
iew
s
o
f
Be
n
&
Je
rry
'
s
ice
c
re
a
m
.
We
d
e
sig
n
e
d
a
n
d
e
v
a
lu
a
ted
th
re
e
m
a
c
h
in
e
lea
rn
in
g
a
lg
o
rit
h
m
s
fo
r
s
e
n
ti
m
e
n
t
c
las
sifica
ti
o
n
:
Na
ïv
e
Ba
y
e
s
(NB),
lo
g
isti
c
re
g
re
ss
io
n
(LR),
a
n
d
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
i
n
e
(S
VM)
.
T
h
e
d
a
tas
e
t
e
x
h
ib
it
e
d
a
sig
n
if
ica
n
t
c
las
s
imb
a
lan
c
e
,
with
su
b
sta
n
t
ially
m
o
re
p
o
siti
v
e
th
a
n
n
e
g
a
ti
v
e
re
v
iew
s.
We
e
m
p
l
o
y
e
d
t
wo
o
v
e
rsa
m
p
li
n
g
tec
h
n
i
q
u
e
s:
th
e
sy
n
th
e
ti
c
m
in
o
rit
y
o
v
e
rsa
m
p
li
n
g
tec
h
n
iq
u
e
(S
M
OTE)
a
n
d
t
h
e
a
d
a
p
ti
v
e
sy
n
th
e
ti
c
sa
m
p
li
n
g
a
p
p
r
o
a
c
h
(AD
ASYN).
Wi
th
th
e
o
rig
i
n
a
l
s
k
e
we
d
d
a
ta,
N
B,
LR,
a
n
d
S
VM
a
c
h
iev
e
d
a
c
c
u
ra
c
ies
o
f
9
1
.
9
0
%
,
9
3
.
7
7
%
,
a
n
d
9
5
.
0
9
%
,
re
sp
e
c
ti
v
e
ly
.
Wh
il
e
S
M
OTE
d
id
n
o
t
imp
r
o
v
e
p
e
rfo
rm
a
n
c
e
in
s
o
m
e
sc
e
n
a
rio
s,
AD
ASYN
y
ield
e
d
p
o
sit
iv
e
re
su
lt
s
a
n
d
g
e
n
e
ra
ll
y
e
n
h
a
n
c
e
d
m
o
d
e
l
re
li
a
b
il
it
y
a
c
ro
ss
a
ll
a
lg
o
rit
h
m
s.
P
o
st
-
b
a
l
a
n
c
i
n
g
wit
h
AD
ASYN,
th
e
se
n
ti
m
e
n
t
d
istri
b
u
t
io
n
b
e
c
a
m
e
le
ss
sk
e
we
d
,
a
n
d
a
c
c
u
ra
c
ies
sh
ift
e
d
to
9
2
.
0
4
%
fo
r
NB,
9
4
.
9
6
%
f
o
r
LR,
a
n
d
9
5
.
2
3
%
fo
r
S
VM.
T
h
e
c
o
m
b
i
n
a
ti
o
n
o
f
S
VM
a
n
d
AD
ASYN
d
e
m
o
n
stra
ted
p
r
o
m
isin
g
re
su
lt
s,
su
g
g
e
sti
n
g
th
is
a
p
p
ro
a
c
h
m
a
y
o
f
fe
r
ro
b
u
st
a
n
d
e
fficie
n
t
p
e
rfo
rm
a
n
c
e
fo
r
b
i
n
a
ry
se
n
ti
m
e
n
t
c
las
sifica
ti
o
n
,
e
sp
e
c
ially
with
imb
a
lan
c
e
d
d
a
tas
e
ts.
K
ey
w
o
r
d
s
:
L
o
g
is
tic
r
eg
r
ess
io
n
Naiv
e
B
ay
es
Pro
d
u
ct
r
ev
iews
Sen
tim
en
t a
n
aly
s
is
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Hasan
Dwi
C
ah
y
o
n
o
Dep
ar
tm
en
t o
f
Data
Scien
ce
,
Facu
lty
o
f
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
a
n
d
Data
Scien
ce
Un
iv
er
s
itas
Seb
elas M
ar
et
Su
r
ak
ar
ta,
C
en
tr
al
J
av
a,
I
n
d
o
n
esia
E
m
ail:
h
asan
.
d
wi.
ca
h
y
o
n
o
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
C
u
s
to
m
er
s
atis
f
ac
tio
n
,
p
r
o
d
u
ct
r
ev
iews,
a
n
d
u
s
er
p
r
ef
er
en
ce
s
ar
e
ex
am
p
les
o
f
u
s
er
-
g
en
er
ated
in
f
o
r
m
atio
n
r
ap
i
d
ly
g
r
o
win
g
an
d
b
ec
o
m
in
g
a
v
alu
a
b
le
s
o
u
r
ce
o
f
b
u
s
in
ess
an
d
m
ar
k
etin
g
in
tellig
en
ce
[
1
]
.
T
h
ese
cu
s
to
m
er
r
ev
iews
h
av
e
b
ec
o
m
e
cr
u
cial
f
o
r
p
r
o
d
u
ct
m
an
u
f
ac
tu
r
e
r
s
,
s
er
v
ice
p
r
o
v
id
er
s
,
an
d
en
d
-
u
s
er
s
to
u
n
d
er
s
tan
d
p
u
b
lic
o
p
in
io
n
an
d
m
ak
e
co
n
cr
ete
d
ec
is
io
n
s
.
C
u
s
to
m
er
r
e
v
iews
o
n
e
-
co
m
m
e
r
ce
p
latf
o
r
m
s
ar
e
a
s
ig
n
if
ican
t
f
ac
to
r
in
co
n
s
u
m
e
r
d
ec
is
io
n
-
m
a
k
in
g
i
n
th
e
m
o
d
e
r
n
d
i
g
ital
ag
e
[
2
]
.
T
h
ese
r
ev
ie
ws
ar
e
n
o
t
o
n
ly
to
p
r
o
v
id
e
p
r
o
s
p
ec
tiv
e
c
u
s
to
m
er
s
with
h
elp
f
u
l
in
f
o
r
m
atio
n
b
u
t
also
to
e
n
ab
le
m
an
u
f
ac
tu
r
er
s
to
g
et
in
s
ig
h
tf
u
l
cr
iticis
m
th
at
h
elp
s
th
em
im
p
r
o
v
e
th
e
s
tan
d
ar
d
o
f
th
eir
g
o
o
d
s
an
d
s
er
v
ices.
T
h
u
s
,
s
en
tim
en
t
an
aly
s
is
is
em
er
g
in
g
as
a
cr
u
cial
to
o
l
f
o
r
d
er
iv
i
n
g
co
n
clu
s
io
n
s
f
r
o
m
th
e
co
n
ten
t
o
f
th
e
r
ev
iews
d
u
e
to
th
e
g
r
o
win
g
n
u
m
b
er
o
f
r
ev
iews
th
at
ar
e
ac
ce
s
s
ib
le
o
n
lin
e
[
3
]
.
E
v
er
y
in
d
u
s
tr
ial
co
m
p
a
n
y
f
ac
es
v
ar
io
u
s
ch
allen
g
es,
esp
ec
ially
th
e
lar
g
e
n
u
m
b
er
o
f
co
m
p
etito
r
s
in
th
e
s
am
e
s
ec
to
r
,
s
u
ch
as
in
th
e
f
o
o
d
an
d
b
ev
er
ag
e
(
F&
B
)
in
d
u
s
t
r
y
.
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
A
s
en
timen
t a
n
a
lysi
s
o
n
s
ke
w
ed
p
r
o
d
u
ct
r
ev
iew
s
:
B
en
&
Je
r
r
y'
s
ice
crea
m
(
N
a
b
illa
N
u
r
u
l
ita
Dewi
)
365
T
h
e
m
an
u
f
ac
tu
r
in
g
s
ec
to
r
m
u
s
t
co
n
tin
u
o
u
s
ly
im
p
r
o
v
e
its
s
er
v
ice
q
u
ality
.
Ser
v
i
ce
q
u
ality
im
p
r
o
v
em
e
n
t
r
ef
er
s
to
th
e
a
b
ilit
y
o
f
a
p
r
o
ce
s
s
to
p
r
o
d
u
ce
a
p
r
o
d
u
ct
o
r
s
er
v
ice
ac
co
r
d
in
g
t
o
th
e
s
p
ec
if
icatio
n
s
d
esire
d
b
y
t
h
e
cu
s
to
m
er
[
4
]
.
Ho
wev
er
,
s
o
m
etim
es
s
itu
atio
n
s
o
cc
u
r
wh
e
r
e
th
e
p
r
o
d
u
cts
m
u
s
t
m
ee
t
cu
s
to
m
er
ex
p
ec
tatio
n
s
wh
en
h
an
d
ed
o
v
er
to
th
em
.
A
f
u
n
d
am
e
n
tal
id
ea
in
s
er
v
ice
m
a
n
ag
em
en
t
an
d
m
ar
k
etin
g
is
cu
s
to
m
er
h
ap
p
in
ess
.
Nu
m
er
o
u
s
th
eo
r
ies
e
x
is
t
co
n
ce
r
n
in
g
cu
s
to
m
er
s
atis
f
ac
tio
n
.
E
x
p
e
ctatio
n
-
d
is
cr
ep
an
cy
th
eo
r
y
,
eq
u
ity
th
eo
r
y
,
attr
i
b
u
ti
o
n
th
e
o
r
y
,
d
is
s
o
n
an
ce
t
h
eo
r
y
,
an
d
co
n
tr
ast
th
eo
r
y
h
av
e
all
b
ee
n
ap
p
lied
to
t
h
e
s
tu
d
y
o
f
co
n
s
u
m
e
r
h
ap
p
i
n
ess
in
th
e
p
ast
[
5
]
.
B
ef
o
r
e
p
u
r
c
h
a
s
in
g
a
p
r
o
d
u
ct
o
r
s
er
v
ice,
c
u
s
to
m
er
s
h
av
e
q
u
ality
ex
p
ec
tatio
n
s
r
eg
ar
d
in
g
t
h
e
p
r
o
d
u
ct
o
r
s
er
v
i
ce
.
Af
ter
m
ak
i
n
g
a
p
u
r
ch
ase,
th
e
y
will
co
m
p
ar
e
ac
tu
al
p
er
ce
p
tio
n
s
with
th
eir
ex
p
ec
tatio
n
s
.
Po
s
itiv
e
m
is
m
atch
o
cc
u
r
s
wh
e
n
th
e
ac
tu
al
p
er
ce
p
tio
n
s
ar
e
h
ig
h
er
th
a
n
th
e
ex
p
ec
tatio
n
.
W
h
en
ac
tu
al
p
er
c
ep
tio
n
s
m
ee
t
ex
p
ec
tatio
n
s
,
th
er
e
is
n
o
m
is
m
atch
o
f
ex
p
ec
tatio
n
s
.
C
o
n
v
er
s
ely
,
a
n
eg
ativ
e
m
is
m
atch
m
ea
n
s
th
at
ac
tu
al
p
er
ce
p
tio
n
s
f
all
b
elo
w
ex
p
ec
tatio
n
s
.
Sen
tim
en
t
an
aly
s
is
h
as
in
cr
e
asin
g
ly
b
ec
o
m
e
cr
u
cial
in
d
e
ter
m
in
in
g
m
a
r
k
et
tr
en
d
s
an
d
co
n
s
u
m
er
f
ee
d
b
ac
k
b
y
id
e
n
tify
in
g
an
d
ca
teg
o
r
izin
g
th
o
u
g
h
ts
with
in
a
te
x
t.
Pas
t
s
tu
d
ies
h
av
e
i
m
p
r
o
v
e
d
s
en
tim
en
t
an
aly
s
is
ac
cu
r
ac
y
a
n
d
ef
f
icie
n
cy
th
r
o
u
g
h
v
ar
io
u
s
ca
teg
o
r
i
za
tio
n
m
eth
o
d
s
[
6
]
.
Fu
r
th
er
m
o
r
e,
a
s
tu
d
y
f
r
o
m
B
ah
tiar
et
a
l.
[
7
]
u
s
ed
s
en
ti
m
en
t
an
aly
s
is
o
n
Go
o
g
le
Play
Sto
r
e
ap
p
r
ev
iews
to
u
n
d
er
s
tan
d
u
s
er
s
'
f
ee
lin
g
s
ab
o
u
t
co
m
m
e
r
cially
s
o
ld
p
r
o
g
r
am
s
u
s
in
g
Naïv
e
B
ay
es
(
N
B
)
an
d
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
.
T
h
e
to
n
e
o
f
ea
ch
r
ev
iew
was
estab
lis
h
ed
b
y
c
o
m
p
ar
in
g
it
to
th
e
ap
p
licatio
n
'
s
r
atin
g
.
T
w
o
co
n
d
itio
n
s
w
er
e
ap
p
lied
to
th
e
d
ataset:
two
lab
els
(
p
o
s
itiv
e
an
d
n
e
g
ativ
e)
a
n
d
th
r
ee
(
p
o
s
itiv
e,
n
eu
tr
al,
a
n
d
n
e
g
ativ
e
)
.
L
R
class
if
icatio
n
y
ield
ed
th
e
b
est
r
esu
lts
f
o
r
th
e
Sh
o
p
ee
d
ataset
with
t
wo
lab
els,
with
8
4
.
5
8
%
ac
cu
r
ac
y
,
8
4
.
6
6
%
p
r
ec
is
io
n
,
an
d
8
4
.
6
3
%
r
ec
all.
T
h
e
s
tu
d
y
s
h
o
wed
th
at
d
atasets
with
two
lab
els
g
en
er
ally
y
ield
ed
m
o
r
e
a
cc
u
r
ate
r
esu
lts
th
an
th
o
s
e
with
th
r
ee
.
B
ased
o
n
th
e
p
r
e
v
io
u
s
s
tu
d
ies,
NB
an
d
L
R
s
h
o
w
p
r
o
m
is
in
g
r
esu
lts
in
s
en
tim
en
t
an
aly
s
is
.
Ho
wev
er
,
b
o
th
alg
o
r
ith
m
s
s
u
f
f
er
ed
p
er
f
o
r
m
an
ce
ch
all
en
g
es,
esp
ec
ially
wh
en
th
e
d
ata
u
s
ed
f
o
r
in
v
esti
g
atio
n
h
as
im
b
alan
ce
d
d
is
tr
ib
u
tio
n
s
(
s
k
ewe
d
)
.
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
is
a
p
r
ac
tic
al
m
ac
h
in
e
-
lear
n
i
n
g
ap
p
r
o
ac
h
to
r
eg
r
ess
io
n
an
d
c
lass
if
icatio
n
p
r
o
b
lem
s
.
SVM
id
en
tifie
s
th
e
p
er
f
ec
t
h
y
p
er
p
lan
e
s
ep
ar
atin
g
all
class
es
f
r
o
m
m
o
s
t
is
o
lated
d
ata
p
o
in
ts
[
8
]
.
As
a
r
esu
lt,
SVM
p
er
f
o
r
m
s
well
in
h
ig
h
-
d
im
en
s
io
n
al
s
ettin
g
s
lik
e
tex
t
d
ata
with
th
o
u
s
an
d
s
o
f
f
e
atu
r
es
an
d
co
m
p
lex
d
ec
is
io
n
b
o
u
n
d
ar
ies.
Als
o
,
k
er
n
el
f
u
n
c
tio
n
s
ca
n
tr
an
s
f
o
r
m
th
e
in
co
m
in
g
d
ata
in
to
h
ig
h
e
r
d
im
en
s
io
n
al
s
p
ac
e,
th
e
r
eb
y
im
p
r
o
v
in
g
class
if
icatio
n
p
er
f
o
r
m
an
ce
s
[
9
]
,
[
1
0
]
.
SVM
h
as
b
ee
n
u
s
ed
in
ca
s
es
wh
er
e
th
e
s
ep
a
r
ab
ilit
y
o
f
class
es
is
n
o
t
lin
ea
r
[
1
1
]
.
An
aly
zi
n
g
cu
s
to
m
er
r
ev
iews
ca
n
h
el
p
im
p
r
o
v
e
p
r
o
d
u
ct
o
f
f
er
s
,
in
c
r
ea
s
e
cu
s
to
m
er
s
atis
f
ac
tio
n
,
an
d
o
f
f
er
in
s
ig
h
tf
u
l
in
f
o
r
m
atio
n
ab
o
u
t
co
n
s
u
m
er
p
r
ef
er
e
n
ce
s
.
Un
d
er
s
tan
d
in
g
s
en
tim
en
t
i
n
ice
cr
ea
m
r
ev
iews
ca
n
also
h
el
p
m
er
c
h
an
ts
an
d
p
r
o
d
u
ce
r
s
s
p
o
t tr
en
d
s
,
r
eso
lv
e
p
r
o
b
lem
s
,
an
d
ad
ju
s
t th
eir
m
ar
k
etin
g
tactics to
s
atis
f
y
cu
s
to
m
er
s
b
etter
.
T
h
is
s
tu
d
y
aim
s
to
im
p
r
o
v
e
s
en
tim
en
t
an
aly
s
is
p
er
f
o
r
m
an
ce
,
esp
ec
ially
f
o
r
im
b
alan
ce
d
d
ata.
W
e
f
o
cu
s
o
n
cu
s
to
m
er
r
ev
iews
o
f
ice
cr
ea
m
p
r
o
d
u
cts.
Ou
r
ap
p
r
o
ac
h
in
v
o
lv
es
ca
r
ef
u
l
d
ata
clea
n
in
g
an
d
u
s
in
g
o
v
er
s
am
p
lin
g
tech
n
iq
u
es
b
ased
o
n
s
y
n
th
etic
m
in
o
r
ity
o
v
e
r
s
am
p
lin
g
tech
n
i
q
u
e
(
SMOT
E
)
an
d
th
e
ad
a
p
tiv
e
s
y
n
th
etic
s
am
p
lin
g
a
p
p
r
o
ac
h
(
ADASYN)
o
v
er
s
am
p
lin
g
m
et
h
o
d
o
lo
g
ies
to
ac
h
iev
e
d
ataset
b
alan
cin
g
.
W
e
th
en
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
NB
,
L
R
,
an
d
SVM
u
s
in
g
s
ev
er
al
m
etr
ics.
Ou
r
g
o
al
is
to
p
r
o
v
id
e
p
r
ac
tical
g
u
id
an
ce
f
o
r
c
h
o
o
s
in
g
th
e
b
est
s
en
tim
en
t a
n
aly
s
is
m
eth
o
d
f
o
r
im
b
alan
ce
d
d
atasets
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
was
co
n
d
u
cted
in
m
u
ltip
le
s
tep
s
,
as
Fig
u
r
e
1
illu
s
tr
ates.
I
n
th
is
wo
r
k
,
we
in
teg
r
ated
d
if
f
er
en
t
ap
p
r
o
ac
h
es
an
d
p
h
ases
o
f
th
e
r
esear
ch
as
o
u
tlin
ed
in
th
e
f
o
llo
win
g
s
tep
s
:
t
o
g
u
a
r
an
tee
th
e
d
ataset'
s
q
u
ality
an
d
alg
o
r
ith
m
ic
c
o
m
p
atib
ilit
y
,
we
f
ir
s
t
g
ath
e
r
ed
an
d
p
r
ep
r
o
ce
s
s
ed
th
e
co
m
b
i
n
atio
n
o
f
p
r
o
d
u
ct
n
am
es
an
d
cu
s
to
m
er
r
ev
iews
to
p
r
e
d
ict
th
e
s
en
tim
en
ts
o
f
cu
s
to
m
er
s
.
T
h
u
s
,
th
e
co
n
s
id
er
atio
n
o
f
m
u
ltico
llin
ea
r
ity
o
f
v
ar
iab
les
ca
n
b
e
r
elax
ed
.
L
astl
y
,
we
tes
ted
an
d
co
m
p
ar
e
d
th
e
r
o
b
u
s
tn
ess
o
f
ea
ch
m
o
d
el
in
an
aly
zin
g
th
e
s
en
tim
en
t
o
f
cu
s
to
m
er
r
ev
ie
ws
b
y
ass
e
s
s
in
g
it
s
p
er
f
o
r
m
an
ce
u
s
in
g
cr
iter
ia
in
clu
d
in
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e.
W
e
u
s
ed
a
W
in
d
o
ws
1
0
Pro
6
4
-
b
it
PC
with
1
3
th
Gen
I
n
tel
C
o
r
e
i9
3
.
0
0
GHz
,
1
2
8
GHz
o
f
R
AM
,
an
d
NVI
DI
A
GeFo
r
ce
R
T
X
4
0
7
0
T
i to
in
v
esti
g
ate
.
2
.
1
.
Da
t
a
s
et
A
d
ataset
r
elate
d
to
c
u
s
to
m
e
r
r
ev
iews
i
n
F&
B
was
in
v
esti
g
ated
u
s
in
g
m
u
ltin
o
m
ial
Na
iv
e
B
ay
es
(
MN
B
)
,
L
R
,
an
d
SVM.
T
h
e
d
is
tin
ct
d
ata
was
co
llected
f
r
o
m
Sep
tem
b
e
r
u
n
til
Octo
b
e
r
2
0
2
0
f
r
o
m
B
en
&
J
er
r
y
’
s
ice
cr
ea
m
.
T
h
is
tim
ef
r
am
e
was
to
en
s
u
r
e
th
at
th
e
d
ata
r
ef
lects
cu
r
r
en
t
co
n
s
u
m
er
o
p
in
io
n
s
an
d
r
esp
o
n
s
es
to
an
y
p
o
ten
tial
p
r
o
d
u
ct
lau
n
ch
es
o
r
m
ar
k
etin
g
ca
m
p
aig
n
s
b
y
B
en
&
J
er
r
y
’
s
.
T
h
e
d
ataset
h
as
7
9
4
3
r
ec
o
r
d
s
with
co
n
s
id
er
ab
ly
h
ig
h
e
r
p
o
s
itiv
e
r
ev
iews
th
an
n
eg
ativ
e
o
n
es.
W
e
u
s
ed
two
s
ec
ti
o
n
s
in
th
e
d
ataset:
th
e
p
r
o
d
u
ct
r
e
v
iew
an
d
th
e
p
r
o
d
u
ct
n
a
m
e.
On
ly
p
o
s
itiv
e
o
r
n
eg
ativ
e
r
ev
iews
wer
e
co
n
s
id
er
ed
to
co
n
ce
n
tr
ate
th
e
s
en
tim
en
t
an
aly
s
is
o
n
d
is
tin
ct
co
n
s
u
m
er
h
ap
p
in
ess
o
r
d
is
s
atis
f
ac
tio
n
m
ar
k
er
s
.
E
x
clu
d
in
g
n
eu
tr
al
r
ev
iews,
wh
ich
f
r
e
q
u
en
tly
p
r
o
v
id
e
h
a
r
d
ly
c
o
m
p
r
e
h
en
s
iv
e
i
n
p
u
t,
m
ad
e
th
e
s
tu
d
y
s
im
p
ler
an
d
g
u
ar
an
teed
th
at
th
e
co
n
clu
s
io
n
s
d
r
awn
wer
e
b
o
t
h
ap
p
licab
le
an
d
ac
cu
r
ately
r
ep
r
esen
ted
th
e
s
tr
o
n
g
o
p
in
io
n
s
o
f
th
e
cu
s
to
m
er
s
.
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.
3
9
,
No
.
1
,
Ju
ly
20
25
:
364
-
3
7
3
366
Fig
u
r
e
1
.
T
h
e
f
lo
wch
a
r
t o
f
o
u
r
r
esear
ch
s
tag
es
2
.
2
.
Da
t
a
p
ro
ce
s
s
ing
C
u
s
to
m
er
r
ev
iews
ar
e
g
e
n
er
al
ly
in
th
e
f
o
r
m
o
f
u
n
s
tr
u
ctu
r
ed
tex
t
d
ata
th
at
o
f
ten
co
n
tain
s
n
o
is
e,
s
u
ch
as
s
p
ellin
g
er
r
o
r
s
an
d
s
y
m
b
o
ls
.
T
h
e
p
r
ep
r
o
ce
s
s
in
g
s
tag
e
was
p
u
r
p
o
s
ed
to
elim
in
ate
n
ee
d
less
wo
r
d
s
an
d
d
ec
r
ea
s
e
th
e
d
ataset'
s
d
im
en
s
io
n
ality
,
m
a
k
in
g
it
ea
s
ie
r
t
o
p
r
o
ce
s
s
in
t
h
e
f
o
llo
win
g
s
tep
.
T
h
is
s
tu
d
y
u
s
ed
s
ev
er
al
p
r
ep
r
o
ce
s
s
in
g
tech
n
iq
u
es,
in
clu
d
in
g
ca
s
e
f
o
ld
in
g
,
clea
n
in
g
,
s
to
p
wo
r
d
r
em
o
v
al
,
to
k
en
izatio
n
,
a
n
d
s
tem
m
in
g
.
C
ase
f
o
ld
in
g
is
r
es
p
o
n
s
ib
le
f
o
r
co
n
v
er
tin
g
all
tex
t
to
lo
wer
ca
s
e
to
en
s
u
r
e
co
n
s
is
ten
cy
o
f
all
wo
r
d
s
in
th
e
tex
t.
C
lean
in
g
m
ea
n
s
clea
n
in
g
tex
t
f
r
o
m
ex
ce
s
s
iv
e
u
s
e
o
f
letter
s
an
d
s
y
m
b
o
ls
b
y
co
r
r
ec
tin
g
co
n
tr
ac
tio
n
s
,
r
em
o
v
in
g
wo
r
d
r
ep
etitio
n
s
,
an
d
ex
ce
s
s
iv
e
p
u
n
ctu
atio
n
.
Sto
p
wo
r
d
r
e
m
o
v
al
elim
in
ates
les
s
ess
en
tial
wo
r
d
s
(
s
to
p
wo
r
d
s
)
to
r
ed
u
ce
th
e
d
im
e
n
s
io
n
ality
o
f
t
h
e
d
ataset.
T
o
k
e
n
izatio
n
s
p
lits
tex
ts
o
r
s
en
ten
ce
s
in
to
in
d
iv
id
u
al
wo
r
d
s
.
Stem
m
i
n
g
is
tr
an
s
f
o
r
m
i
n
g
wo
r
d
s
in
th
e
tex
t to
th
eir
b
ase
f
o
r
m
.
2
.
3
.
Da
t
a
s
et
b
a
la
ncing
I
n
th
is
s
tu
d
y
,
th
e
d
ataset
u
s
ed
ex
p
er
ien
ce
d
d
ata
im
b
alan
c
e,
wh
er
e
th
e
n
u
m
b
er
o
f
s
am
p
les
in
o
n
e
ca
teg
o
r
y
(
n
eg
ativ
e
ca
te
g
o
r
y
)
is
m
u
ch
less
th
an
th
e
p
o
s
itiv
e
ca
teg
o
r
y
.
W
h
ile
th
er
e
a
r
e
6
,
4
0
1
g
o
o
d
r
atin
g
s
,
th
er
e
wer
e
1
,
1
3
5
n
e
g
ativ
e
o
n
es.
T
h
ese
p
r
o
p
o
r
tio
n
s
b
etwe
en
a
d
ataset'
s
p
o
s
itiv
e
an
d
n
e
g
ativ
e
r
atin
g
s
wer
e
ty
p
ically
an
im
b
alan
ce
ca
s
e
.
T
h
e
im
b
alan
ce
co
n
d
itio
n
m
ay
af
f
ec
t
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
b
y
d
is
to
r
tin
g
p
r
ed
ictio
n
o
u
tco
m
es
as
th
e
m
o
d
el
ten
d
s
to
b
e
m
o
r
e
ac
c
u
r
at
e
o
n
th
e
m
ajo
r
ity
class
an
d
i
g
n
o
r
e
th
e
m
in
o
r
ity
class
.
T
o
in
v
esti
g
ate
t
h
e
im
b
alan
ce
im
p
ac
t
o
f
th
is
is
s
u
e,
t
h
is
r
esear
ch
u
s
ed
th
e
o
v
er
s
a
m
p
lin
g
a
p
p
r
o
ac
h
in
co
m
b
in
atio
n
with
th
e
SMOT
E
an
d
ADASYN
f
o
r
im
b
alan
ce
lear
n
in
g
.
T
h
e
SMOT
E
tech
n
i
q
u
e
in
c
r
ea
s
es
th
e
s
am
p
le
s
ize
in
th
e
m
in
o
r
ity
class
b
y
g
en
er
atin
g
n
ew
in
s
tan
ce
s
b
ased
o
n
e
x
is
tin
g
d
ata.
Usi
n
g
m
in
o
r
ity
ex
am
p
les
as
a
s
tar
tin
g
p
o
in
t,
th
is
m
eth
o
d
g
e
n
e
r
ates
n
ew
s
y
n
th
etic
cas
es
co
m
p
ar
ab
le
to
y
et
d
is
tin
ct
f
r
o
m
th
e
o
r
ig
in
al
ex
am
p
le
s
.
T
h
is
m
eth
o
d
h
elp
s
to
b
alan
ce
th
e
m
ajo
r
ity
a
n
d
m
in
o
r
ity
class
es,
wh
ich
is
ex
p
ec
ted
to
im
p
r
o
v
e
th
e
class
if
icatio
n
m
o
d
el'
s
ca
p
ac
ity
to
d
is
tin
g
u
is
h
b
etwe
en
p
o
s
itiv
e
an
d
n
e
g
ativ
e
r
atin
g
s
m
o
r
e
ac
c
u
r
ately
[
1
2
]
.
O
n
th
e
o
th
er
h
a
n
d
,
ADASYN
is
a
s
am
p
lin
g
a
p
p
r
o
ac
h
in
v
o
lv
in
g
lear
n
in
g
s
tep
s
.
ADASYN
ca
n
ad
ju
s
t
th
e
weig
h
t
d
is
tr
ib
u
tio
n
s
o
f
m
in
o
r
ity
cla
s
s
es
b
ased
o
n
th
eir
lear
n
in
g
d
if
f
icu
lties
.
Su
b
s
eq
u
en
tly
,
m
o
r
e
d
ata
ar
e
s
y
n
th
es
ized
f
o
r
th
e
m
in
o
r
ity
class
,
wh
ich
h
as
h
i
g
h
er
lear
n
in
g
d
if
f
icu
lties
.
T
h
is
lear
n
in
g
-
b
ased
s
y
n
th
esized
ap
p
r
o
ac
h
is
co
n
s
id
er
ab
ly
b
en
e
f
icial
in
th
e
p
r
esen
ce
o
f
h
ig
h
b
ias,
wh
ich
is
ty
p
ical
f
o
r
im
b
alan
ce
d
d
atasets
[
1
3
]
.
2
.
4
.
Resea
rc
h
m
e
t
ho
ds
SVM
,
L
R
,
an
d
NB
ar
e
th
e
class
if
icatio
n
tech
n
iq
u
es
em
p
lo
y
e
d
in
th
is
r
esear
ch
.
NB
is
a
s
tr
aig
h
tf
o
r
war
d
p
r
o
b
a
b
ilis
tic
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e
b
ased
o
n
th
e
id
ea
s
o
f
f
ea
tu
r
e
in
d
ep
en
d
e
n
ce
an
d
t
h
e
B
ay
es
th
eo
r
em
.
NB
is
a
p
o
p
u
lar
s
o
lu
tio
n
f
o
r
class
if
icatio
n
is
s
u
es
an
d
ex
ce
ls
in
h
ig
h
-
d
im
en
s
io
n
al
f
ea
tu
r
e
s
p
ac
es.
A
r
esp
o
n
s
e
v
ar
iab
le
w
ith
two
o
r
m
o
r
e
ca
teg
o
r
ies,
y
,
an
d
o
n
e
o
r
m
o
r
e
co
n
tin
u
o
u
s
p
r
ed
icto
r
v
ar
iab
les,
x
,
ar
e
r
elate
d
.
T
h
is
r
elatio
n
s
h
ip
ca
n
b
e
ex
p
lain
e
d
u
s
in
g
lo
g
is
tic
r
e
g
r
ess
io
n
.
R
eg
r
e
s
s
io
n
an
aly
s
is
an
d
class
if
icatio
n
ar
e
two
co
m
m
o
n
u
s
es
f
o
r
SVM,
a
well
-
k
n
o
wn
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e.
SVMs
p
er
f
o
r
m
ex
ce
p
tio
n
ally
well
in
h
ig
h
-
d
i
m
en
s
io
n
al
d
o
m
ain
s
an
d
ca
n
h
an
d
le
ch
allen
g
in
g
class
if
icat
io
n
task
s
.
T
o
d
iv
id
e
th
e
b
o
r
d
er
s
o
f
v
ar
io
u
s
class
es,
SVM
cr
ea
tes
a
h
y
p
er
p
la
n
e
in
m
u
ltid
im
en
s
io
n
al
s
p
ac
e.
T
h
e
s
cik
it
-
lear
n
m
ac
h
in
e
lear
n
in
g
to
o
l
k
it a
n
d
t
h
e
Py
th
o
n
p
r
o
g
r
am
m
i
n
g
lan
g
u
ag
e
ar
e
u
s
ed
t
o
im
p
lem
en
t t
h
e
s
e
tech
n
iq
u
es.
2.
4
.
1
.
Na
iv
e
B
a
y
es
T
h
e
m
o
s
t su
b
s
tan
tiv
e
p
r
o
b
a
b
ilit
y
,
h
o
wev
er
,
is
estab
lis
h
ed
th
r
o
u
g
h
a
tech
n
iq
u
e
ca
lled
th
e
N
B
s
tr
ateg
y
th
at
also
ca
teg
o
r
izes te
s
t
d
ata
i
n
to
th
e
m
o
s
t
ap
p
r
o
p
r
iate
class
es.
B
ein
g
a
s
im
p
le
p
r
o
b
ab
ilis
ti
c
m
ac
h
in
e
lear
n
in
g
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
A
s
en
timen
t a
n
a
lysi
s
o
n
s
ke
w
ed
p
r
o
d
u
ct
r
ev
iew
s
:
B
en
&
Je
r
r
y'
s
ice
crea
m
(
N
a
b
illa
N
u
r
u
l
ita
Dewi
)
367
tech
n
iq
u
e,
th
e
NB
class
if
ier
i
s
b
ased
o
n
f
ea
t
u
r
e
i
n
d
ep
e
n
d
e
n
ce
an
d
th
e
B
ay
es
th
eo
r
e
m
.
A
well
-
k
n
o
wn
NB
p
r
o
b
a
b
ilit
y
class
if
ier
th
at
a
p
p
lies
B
ay
es’
th
eo
r
em
is
p
o
p
u
lar
ly
u
s
ed
d
u
e
t
o
its
s
im
p
l
e
a
p
p
licab
ilit
y
an
d
ef
f
ec
tiv
en
ess
o
n
tex
ts
[
1
4
]
.
T
h
e
ass
u
m
p
tio
n
th
at
o
n
e
f
ea
tu
r
e
d
o
es
n
o
t
af
f
ec
t
an
o
th
er
with
in
th
e
s
am
e
class
s
im
p
lifie
s
th
e
co
m
p
u
tatio
n
p
r
o
ce
s
s
.
Desp
ite
h
av
in
g
h
ig
h
in
d
ep
en
d
e
n
ce
r
eq
u
ir
e
m
en
ts
,
NB
r
em
ain
s
attr
ac
tiv
e
f
o
r
s
en
tim
en
t a
n
al
y
s
is
s
in
c
e
it o
f
ten
p
e
r
f
o
r
m
s
s
tr
o
n
g
ly
in
tex
t
class
if
icatio
n
task
s
[
1
5
]
.
Mo
r
eo
v
er
,
NB
is
p
ar
ticu
lar
ly
s
u
ited
f
o
r
r
ea
l
-
tim
e
ap
p
licati
o
n
s
r
eq
u
ir
in
g
s
wif
t
r
esp
o
n
s
es
d
u
e
to
its
p
r
o
ce
s
s
in
g
ef
f
icien
c
y
[
1
6
]
.
N
B
is
co
m
m
o
n
ly
a
p
p
lied
f
o
r
class
if
icatio
n
p
r
o
b
lem
s
an
d
p
e
r
f
o
r
m
s
well
in
h
ig
h
-
d
im
en
s
io
n
al
f
ea
tu
r
e
s
p
ac
e
[
1
7
]
.
T
h
e
class
if
ier
'
s
n
am
e
is
d
er
iv
ed
f
r
o
m
th
e
"
n
aiv
e"
ass
u
m
p
tio
n
th
at
ev
e
r
y
f
ea
tu
r
e
ass
ig
n
ed
a
class
lab
el
is
in
d
ep
en
d
en
t
o
f
ev
er
y
o
t
h
e
r
f
ea
tu
r
e.
NB
u
s
es
r
ela
tiv
ely
litt
le
tr
ain
in
g
d
ata
r
elativ
e
to
o
th
er
class
if
ier
s
an
d
is
a
c
o
m
p
u
tatio
n
ally
ef
f
icien
t
class
if
ier
.
T
h
e
y
w
o
r
k
well
w
h
en
t
h
e
in
d
ep
en
d
en
ce
c
r
iter
io
n
is
m
e
t,
o
r
t
h
e
ap
p
r
o
x
im
ate
co
r
r
ela
tio
n
b
etwe
en
ch
ar
ac
ter
is
tics
ca
n
b
e
d
eter
m
in
e
d
.
T
h
ese
class
if
ier
s
h
av
e
b
ee
n
s
h
o
wn
to
p
er
f
o
r
m
s
tr
o
n
g
ly
in
alm
o
s
t
all
r
ea
l
-
wo
r
ld
a
p
p
licat
io
n
s
,
wh
ich
in
cl
u
d
e
s
en
tim
en
t
an
aly
s
is
,
s
p
am
f
ilte
r
in
g
,
te
x
t
ca
teg
o
r
izatio
n
,
an
d
m
ed
ical
d
iag
n
o
s
is
.
T
h
e
MN
B
tech
n
iq
u
e
is
o
n
e
o
f
th
e
s
u
cc
ess
f
u
l
v
ar
ian
ts
o
f
th
is
alg
o
r
ith
m
th
at
wo
r
k
s
b
y
u
s
in
g
wo
r
d
f
r
eq
u
e
n
cy
s
tatis
tics
—
f
o
r
in
s
tan
ce
,
a
b
in
ar
y
v
ec
to
r
in
th
e
wo
r
d
s
p
ac
e
[
1
8
]
.
Un
lik
e
th
e
m
u
ltiv
ar
iate
B
er
n
o
u
lli
ev
en
t
m
o
d
el,
th
is
MN
B
s
tr
ateg
y
ass
u
m
es th
at
d
o
cu
m
en
t d
u
r
ati
o
n
s
ar
e
in
d
e
p
en
d
e
n
t o
f
t
h
e
cla
s
s
lab
els with
in
th
e
d
o
cu
m
en
ts
.
2.
4
.
2
.
L
o
g
is
t
ic
r
eg
re
s
s
io
n
T
h
e
ap
p
licatio
n
o
f
th
e
L
R
m
eth
o
d
in
th
e
an
al
y
s
is
is
a
way
to
estab
lis
h
a
r
elatio
n
b
etw
ee
n
o
n
e
o
r
m
o
r
e
co
n
tin
u
o
u
s
p
r
ed
icto
r
v
a
r
iab
les,
x
,
an
d
a
r
esp
o
n
s
e
v
a
r
iab
le
th
at
h
as
two
o
r
m
o
r
e
ca
teg
o
r
ies,
y
,
[
1
9
]
.
Desp
ite
b
ein
g
in
itially
d
e
v
el
o
p
ed
f
o
r
n
u
m
e
r
ic
p
r
e
d
ictio
n
,
L
R
h
as
b
ee
n
s
u
cc
ess
f
u
lly
u
s
ed
in
s
e
n
tim
en
t
an
aly
s
is
.
A
s
tatis
tical
ap
p
r
o
ac
h
th
at
m
o
d
els
a
r
elatio
n
s
h
ip
b
etwe
en
o
n
e
o
r
m
o
r
e
in
d
e
p
en
d
en
t
v
ar
iab
les
an
d
th
e
lo
g
it
f
u
n
ctio
n
o
f
th
e
d
e
p
en
d
e
n
t
v
ar
iab
le
to
p
r
e
d
ict
th
e
p
r
o
b
a
b
ilit
y
o
f
a
b
in
a
r
y
e
v
en
t
[
2
0
]
.
L
R
ca
n
class
if
y
tex
t
d
ata
in
to
p
o
s
itiv
e
o
r
n
eg
ativ
e
attitu
d
es
b
y
co
n
v
er
tin
g
a
lin
ea
r
co
m
b
in
atio
n
o
f
in
p
u
t
attr
i
b
u
tes
in
to
lik
elih
o
o
d
.
On
e
s
u
ch
ch
an
g
e
t
h
at
en
h
an
c
es
its
tex
t
d
ata
p
r
o
ce
s
s
in
g
ef
f
icien
cy
is
r
e
g
u
lar
izatio
n
[
2
1
]
,
[
2
2
]
.
Fu
r
th
er
m
o
r
e
,
s
in
ce
L
R
is
in
ter
p
r
etab
le,
u
n
d
er
s
tan
d
i
n
g
h
o
w
ea
c
h
as
p
ec
t
in
f
lu
e
n
ce
s
th
e
p
r
e
d
ictio
n
is
m
o
s
t
lik
ely
s
tr
aig
h
tf
o
r
war
d
[
2
3
]
.
SVM
is
a
p
r
ac
tical
m
ac
h
in
e
-
lear
n
in
g
ap
p
r
o
ac
h
to
r
e
g
r
ess
io
n
an
d
class
if
icatio
n
p
r
o
b
lem
s
.
An
o
th
er
v
ital
as
s
u
m
p
tio
n
to
th
e
L
R
an
aly
s
is
is
th
at
th
er
e
s
h
o
u
ld
b
e
n
o
,
o
r
m
in
im
al,
m
u
ltico
llin
ea
r
ity
o
r
li
n
ea
r
s
o
li
d
r
elatio
n
s
h
ip
b
etwe
en
th
e
p
r
ed
icto
r
v
a
r
iab
les
to
av
o
id
is
s
u
es
wh
en
esti
m
atin
g
co
ef
f
icien
ts
[
2
4
]
,
[
2
5
]
.
Similar
ly
,
h
ig
h
er
co
m
p
lex
ity
m
o
d
el
s
m
ay
p
er
f
o
r
m
b
etter
th
an
L
R
at
h
an
d
lin
g
s
u
ch
ca
s
es.
2.
4
.
3
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
T
h
e
SVM
is
th
e
p
r
o
m
in
en
t
m
eth
o
d
o
f
m
ac
h
in
e
lear
n
in
g
,
ty
p
ically
u
s
ed
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
an
aly
s
is
.
SVM
ass
ess
es
th
e
in
f
o
r
m
atio
n
to
id
e
n
t
if
y
th
e
p
atter
n
o
r
b
o
u
n
d
ar
ies
th
at
p
o
in
t
t
o
war
d
s
ch
o
ices
m
ad
e
with
in
a
d
ataset.
T
o
s
ep
ar
ate
th
e
d
if
f
er
en
t
class
b
o
r
d
er
s
,
SVM
g
en
er
at
es
h
y
p
er
p
lan
es
in
a
m
u
ltid
im
en
s
io
n
al
s
p
ac
e,
th
e
n
u
m
b
er
o
f
wh
ic
h
is
r
ef
er
r
ed
to
as
th
e
f
ea
tu
r
e
v
ec
to
r
o
f
th
e
d
at
aset.
Fu
r
th
er
,
SVM
ca
n
h
an
d
le
v
er
y
c
h
allen
g
in
g
class
if
icatio
n
p
r
o
b
lem
s
an
d
wo
r
k
s
ex
ce
llen
tly
in
h
i
g
h
-
d
im
en
s
io
n
al
s
p
ac
es.
Su
p
p
o
r
t
v
ec
to
r
s
a
r
e
th
e
d
ata
p
o
in
ts
ly
in
g
clo
s
est
to
th
e
p
la
n
e
an
d
wh
at
t
h
is
alg
o
r
ith
m
u
s
es
to
d
eter
m
in
e
th
e
p
o
s
itio
n
in
g
an
d
o
r
ien
tatio
n
[
2
6
]
.
SVM
co
m
es in
a
v
ar
iety
o
f
f
o
r
m
s
.
L
in
ea
r
SVM
is
u
s
ed
f
o
r
lin
ea
r
ly
s
ep
ar
ab
le
d
ata,
wh
er
ea
s
non
-
lin
ea
r
SV
M
m
ap
s
d
ata
in
to
h
ig
h
er
-
d
i
m
en
s
io
n
al
s
p
ac
es
wh
er
e
a
lin
ea
r
h
y
p
er
p
lan
e
ca
n
s
ep
ar
ate
th
e
d
ata
[
2
7
]
.
2
.
5
.
E
v
a
lua
t
i
o
n a
nd
i
nte
rpre
t
a
t
io
n
T
h
ese
m
eth
o
d
s
wer
e
in
v
esti
g
ated
u
s
in
g
t
h
e
Py
th
o
n
p
r
o
g
r
a
m
m
in
g
lan
g
u
ag
e
an
d
lib
r
ar
ie
s
s
u
ch
as
s
cik
it
-
lear
n
f
o
r
m
ac
h
i
n
e
lear
n
in
g
.
I
m
p
lem
en
tatio
n
s
tep
s
in
c
lu
d
ed
s
p
litt
in
g
th
e
d
ata
in
to
tr
ain
in
g
,
v
alid
atio
n
,
an
d
test
in
g
s
ets
o
f
8
0
%,
1
0
%,
an
d
1
0
%,
r
esp
ec
tiv
ely
.
T
o
f
i
n
d
th
e
m
o
s
t
ap
p
r
o
p
r
iate
h
y
p
er
p
ar
am
eter
s
o
f
ea
c
h
class
if
icatio
n
m
o
d
el
d
u
r
in
g
th
e
m
o
d
el
b
u
ild
in
g
,
we
u
s
ed
a
g
r
ee
d
y
-
b
ased
cr
o
s
s
-
v
alid
atio
n
a
lg
o
r
ith
m
b
ased
o
n
Gr
id
Sear
ch
C
V.
Data
b
alan
cin
g
was
ap
p
lied
b
e
f
o
r
e
a
n
d
a
f
ter
m
o
d
el
tr
ai
n
in
g
t
o
co
m
p
ar
e
p
er
f
o
r
m
an
ce
.
T
h
e
test
in
g
s
et
was
u
s
ed
with
ev
alu
atio
n
m
etr
ics
s
u
ch
as
tr
ain
i
n
g
tim
e,
ac
cu
r
ac
y
,
p
r
e
cisi
o
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
to
m
e
a
s
u
r
e
e
a
c
h
m
o
d
e
l'
s
p
e
r
f
o
r
m
a
n
c
e
.
A
c
c
u
r
a
c
y
,
p
r
e
c
is
i
o
n
,
r
e
c
al
l
,
a
n
d
F1
-
s
c
o
r
e
c
a
l
c
u
la
t
i
o
n
a
r
e
e
x
p
r
e
s
s
e
d
i
n
[
2
8
]
.
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
Usi
n
g
an
in
v
esti
g
atio
n
f
r
am
e
wo
r
k
,
we
m
a
d
e
th
e
n
ec
ess
ar
y
ad
ju
s
tm
en
ts
to
s
u
p
p
o
r
t
an
d
i
n
itiate
o
u
r
s
tu
d
y
.
T
h
e
ea
r
lier
in
v
esti
g
ati
o
n
f
o
u
n
d
th
at
NB
an
d
L
R
w
er
e
ad
eq
u
ate
f
o
r
th
e
class
if
icatio
n
.
T
h
u
s
,
in
th
is
s
tu
d
y
,
SMOT
E
an
d
SVM
wer
e
in
v
esti
g
ated
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
ea
r
lier
r
es
ea
r
ch
.
T
h
e
o
b
tain
e
d
r
esu
lts
m
u
s
t
f
ir
s
t
u
n
d
er
tak
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
f
ea
t
u
r
es,
an
d
m
o
d
els
s
u
ch
as
th
e
Naiv
e
-
B
ay
es,
L
R
,
S
V
M
a
l
g
o
r
i
th
m
s
,
d
at
a
b
a
l
an
c
i
n
g
,
an
d
m
o
d
e
l
v
a
l
id
a
t
i
o
n
.
N
a
i
v
e
-
B
a
y
e
s
,
L
R
,
an
d
S
VM
a
l
g
o
r
i
th
m
s
w
e
r
e
a
p
p
l
i
e
d
f
o
r
th
e
s
e
n
t
i
m
en
t
a
n
al
y
s
i
s
o
f
B
e
n
&
J
e
r
r
y
’
s
i
ce
c
r
e
a
m
p
r
o
d
u
c
t
s
w
i
th
o
u
t
a
n
d
w
it
h
d
a
t
a
b
a
l
an
c
in
g
.
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.
3
9
,
No
.
1
,
Ju
ly
20
25
:
364
-
3
7
3
368
M
a
k
i
n
g
t
h
e
r
e
s
u
l
t
s
o
f
t
h
i
s
s
t
u
d
y
r
e
p
r
o
d
u
c
ib
l
e
a
n
d
r
e
p
l
i
c
ab
le
,
t
h
e
m
a
t
e
r
i
a
l
s
w
i
th
t
h
e
ac
c
es
s
i
b
l
e
s
o
u
r
ce
c
o
d
e
a
r
e
av
a
i
l
ab
l
e
a
t
[
2
9
]
.
3
.
1
.
Da
t
a
prepa
ra
t
i
o
n,
m
u
lt
i
co
llin
ea
rit
y
inv
estig
a
t
io
n,
a
n
d ba
la
ncing
Usi
n
g
lab
els
f
r
o
m
th
e
B
en
&
J
er
r
y
’
s
I
ce
C
r
ea
m
Data
s
et
b
as
ed
o
n
th
e
s
tar
r
atin
g
an
d
a
c
o
m
b
in
atio
n
o
f
p
r
o
d
u
ct
n
a
m
e
a
n
d
r
ev
ie
w
tex
t,
s
en
tim
en
t
an
aly
s
is
o
f
th
e
co
m
p
a
n
y
ice
cr
ea
m
p
r
o
d
u
ct
r
ev
iews
was
p
er
f
o
r
m
ed
u
s
in
g
t
h
is
m
o
d
el.
W
e
co
n
v
er
ted
th
e
s
tar
r
atin
g
i
n
to
s
en
tim
en
t
b
y
ex
cl
u
d
in
g
th
e
n
eu
tr
al
o
n
es.
Nex
t,
we
p
er
f
o
r
m
ed
tex
t
p
r
e
p
r
o
ce
s
s
in
g
o
n
th
e
o
n
ly
p
r
ed
icto
r
b
ased
o
n
p
r
o
d
u
ct
n
am
es
an
d
r
ev
iews.
T
h
u
s
,
n
o
f
u
r
th
e
r
s
tep
s
wer
e
tak
en
to
h
an
d
le
th
e
m
u
ltico
llin
ea
r
ity
as
n
o
o
t
h
er
f
ea
tu
r
e
was
in
v
o
lv
ed
.
R
eg
ar
d
in
g
class
im
b
alan
ce
,
to
im
p
r
o
v
e
th
e
r
esu
lts
,
th
e
S
MO
T
E
tech
n
iq
u
e
was
em
p
lo
y
ed
to
ad
d
r
ess
th
e
is
s
u
e
o
f
cl
ass
d
is
p
ar
ity
.
Un
lik
e
m
eth
o
d
s
th
at
o
v
er
s
am
p
le
th
e
m
in
o
r
ity
class
b
y
r
an
d
o
m
ly
r
e
-
s
am
p
lin
g
th
e
m
in
o
r
ity
class
d
ata,
SMOT
E
cr
ea
tes
s
y
n
th
etic
s
am
p
les f
o
r
th
e
m
i
n
o
r
ity
class
[
1
2
]
.
3
.
2
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n a
nd
inte
rpre
t
a
t
io
n:
co
m
pa
ri
s
o
n o
f
s
ent
im
ent
a
na
ly
s
is
a
lg
o
rit
hm
s
T
h
e
an
al
y
s
is
in
clu
d
ed
s
en
tim
en
t
an
aly
s
is
with
NB
,
L
R
,
a
n
d
SVM
in
s
tr
u
m
en
ts
.
B
ased
o
n
u
n
ev
e
n
s
am
p
les,
th
e
d
ata
is
ca
teg
o
r
iz
ed
th
r
o
u
g
h
th
e
NB
class
if
ier
with
th
e
h
el
p
o
f
co
n
d
itio
n
al
p
r
o
b
ab
ilit
y
t
h
eo
r
y
.
I
n
s
tatis
t
ics,
L
R
is
a
v
alu
ab
le
to
o
l
th
at
allo
ws
a
lo
g
is
t
ic
f
u
n
ctio
n
to
ass
ess
p
r
o
b
ab
ilit
ies
an
d
p
r
ed
ict
th
is
o
r
th
at
b
in
ar
y
e
v
en
t.
On
th
e
o
th
er
h
an
d
,
th
e
SVM
class
if
icatio
n
ap
p
r
o
ac
h
is
b
ased
o
n
d
eter
m
in
in
g
t
h
e
h
y
p
e
r
p
lan
e
t
h
at
b
est s
ep
ar
ates d
ata
p
o
in
ts
with
d
if
f
er
en
t c
lass
if
icatio
n
s
with
th
e
m
o
s
t sig
n
if
ican
t m
a
r
g
in
.
As
r
ep
o
r
ted
in
T
ab
le
1
,
th
e
NB
alg
o
r
ith
m
h
ad
a
s
en
tim
e
n
t
d
is
tr
ib
u
tio
n
o
f
9
6
%
p
o
s
itiv
e
an
d
4
%
n
eg
ativ
e,
t
h
e
L
R
alg
o
r
ith
m
wa
s
8
9
%
p
o
s
itiv
e
a
n
d
1
1
%
n
e
g
at
iv
e,
an
d
th
e
SVM
alg
o
r
ith
m
h
ad
a
d
is
tr
ib
u
tio
n
o
f
8
6
%
p
o
s
itiv
e
an
d
1
4
%
n
e
g
ativ
e.
Af
ter
th
e
d
ata
b
ala
n
cin
g
p
r
o
ce
s
s
with
SMOT
E
,
th
e
NB
,
an
d
L
R
alg
o
r
ith
m
s
h
ad
a
s
en
tim
en
t d
is
tr
ib
u
tio
n
o
f
8
1
% p
o
s
itiv
e
an
d
1
9
% n
eg
ati
v
e.
T
h
e
SVM
alg
o
r
ith
m
h
ad
a
n
8
4
%
p
o
s
itiv
e
a
n
d
1
6
%
n
eg
ativ
e
d
is
tr
ib
u
tio
n
.
W
i
th
ADASYN,
NB
r
ea
ch
ed
8
0
%
p
o
s
itiv
e
an
d
2
0
%
n
eg
ativ
e,
wh
ile
L
R
an
d
SVM
h
av
e
s
im
ilar
f
ig
u
r
es
at
8
4
%
p
o
s
itiv
e
an
d
1
6
%
n
eg
ativ
e.
Ov
er
all,
th
e
d
ata
b
alan
cin
g
p
r
o
c
ess
u
s
in
g
SMOT
E
an
d
ADASYN
r
ed
u
ce
d
th
e
im
b
alan
ce
in
s
en
tim
en
t
d
is
tr
ib
u
tio
n
.
T
ab
le
1
.
T
h
e
p
er
f
o
r
m
a
n
ce
co
m
p
ar
is
o
n
o
f
s
en
tim
en
t d
is
tr
ib
u
tio
n
s
with
o
u
t d
ata
b
alan
ci
n
g
(
w
ith
o
u
t)
,
u
s
in
g
SMOT
E
an
d
ADASYN
d
ata
b
alan
cin
g
b
etwe
en
p
o
s
itiv
e
r
ev
i
ews (
p
o
s
)
an
d
n
eg
ativ
e
o
n
es (
n
eg
)
D
a
t
a
b
a
l
a
n
c
i
n
g
NB
LR
S
V
M
W
i
t
h
o
u
t
S
M
O
TE
A
D
A
S
Y
N
T
ab
le
2
s
h
o
ws
th
e
m
o
d
el
p
er
f
o
r
m
an
ce
ev
alu
atio
n
r
esu
lts
.
W
ith
o
u
t
d
ata
b
alan
cin
g
,
th
e
SVM
alg
o
r
ith
m
s
h
o
wed
t
h
e
b
est
s
en
tim
en
t
an
aly
s
is
p
er
f
o
r
m
an
ce
with
an
ac
cu
r
ac
y
o
f
9
5
.
0
9
%,
a
tr
ain
in
g
tim
e
o
f
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
A
s
en
timen
t a
n
a
lysi
s
o
n
s
ke
w
ed
p
r
o
d
u
ct
r
ev
iew
s
:
B
en
&
Je
r
r
y'
s
ice
crea
m
(
N
a
b
illa
N
u
r
u
l
ita
Dewi
)
369
0
.
1
4
s
ec
o
n
d
s
,
a
p
r
ec
is
io
n
o
f
9
5
.
0
%,
a
r
ec
all
o
f
9
5
.
1
%,
an
d
an
F1
-
s
co
r
e
o
f
9
4
.
9
%.
Af
t
er
in
teg
r
atin
g
d
ata
b
alan
cin
g
u
s
in
g
SMOT
E
,
th
e
SVM
alg
o
r
ith
m
s
h
o
wed
a
s
lig
h
t
d
ec
r
ea
s
e
in
p
er
f
o
r
m
a
n
ce
with
an
ac
cu
r
ac
y
o
f
9
4
.
9
6
%,
a
tr
ai
n
in
g
tim
e
o
f
0
.
2
1
s
ec
o
n
d
s
,
a
s
tab
le
p
r
ec
is
io
n
o
f
9
5
.
0
%,
a
r
ec
all
o
f
9
5
.
0
%,
an
d
an
im
p
r
o
v
ed
F1
-
s
co
r
e
o
f
9
5
.
0
%.
Similar
ly
,
L
R
s
u
f
f
er
ed
a
p
er
f
o
r
m
an
ce
d
e
cr
ea
s
e
in
ac
c
u
r
ac
y
f
r
o
m
9
3
.
7
7
%
to
9
3
.
6
3
%
af
ter
d
ata
b
alan
cin
g
u
s
in
g
SMOT
E
.
Ho
wev
er
,
th
e
p
r
ec
is
io
n
s
co
r
e
was
r
aised
to
9
5
.
0
%,
wh
ic
h
is
co
m
p
ar
ab
le
t
o
SVM
'
s
.
E
ith
er
NB
o
r
L
R
h
as
th
e
f
astes
t
tr
ain
in
g
tim
e
an
d
is
n
ea
r
ly
in
s
tan
t,
m
ak
i
n
g
b
o
th
m
eth
o
d
s
p
r
o
m
is
in
g
alg
o
r
ith
m
s
f
o
r
r
ea
l
-
tim
e
s
en
tim
en
t
an
aly
s
is
.
Ap
p
ly
in
g
SMOT
E
an
d
ADASYN
g
en
e
r
ally
in
cr
ea
s
ed
th
e
p
r
o
ce
s
s
in
g
tim
e
ac
r
o
s
s
all
th
r
ee
alg
o
r
ith
m
s
.
B
ased
o
n
th
e
c
o
n
f
u
s
io
n
m
atr
ices
in
T
ab
le
3
,
ap
p
ly
in
g
SMOT
E
an
d
ADASYN
also
ty
p
ically
d
ev
elo
p
s
t
h
e
d
etec
tio
n
o
f
th
e
m
in
o
r
ity
class
(
n
eg
ativ
e
s
am
p
les)
ac
r
o
s
s
all
th
r
ee
alg
o
r
ith
m
s
.
C
o
n
tr
a
r
ily
,
th
e
d
e
v
elo
p
m
en
t
o
f
th
e
m
i
n
o
r
ity
class
m
ay
h
av
e
af
f
ec
ted
th
e
tr
u
e
n
eg
ativ
e
(
T
N)
a
n
d
f
alse
p
o
s
itiv
e
(
FP
)
r
ates.
Ov
er
all,
SVM
s
h
o
ws
r
elativ
ely
s
ta
b
le
p
er
f
o
r
m
an
ce
with
m
in
o
r
u
p
d
ates
o
n
b
o
th
th
e
T
P a
n
d
T
N,
m
ak
i
n
g
it st
ill a
r
o
b
u
s
t c
h
o
ice
f
o
r
im
b
alan
ce
d
d
at
asets
in
s
en
tim
en
t a
n
aly
s
is
.
T
ab
le
2
.
C
o
m
p
a
r
is
o
n
o
f
th
e
al
g
o
r
ith
m
p
er
f
o
r
m
an
ce
i
n
tr
ain
i
n
g
tim
e
in
s
ec
o
n
d
(
s
)
,
ac
c
u
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
A
l
g
o
r
i
t
h
m
D
a
t
a
b
a
l
a
n
c
i
n
g
Tr
a
i
n
i
n
g
t
i
me
(
s)
Te
st
i
n
g
r
e
su
l
t
s
A
c
c
u
r
a
c
y
(
%)
P
r
e
c
i
s
i
o
n
(
%)
Rec
a
l
l
(
%)
F1
-
s
c
o
r
e
(
%)
NB
W
i
t
h
o
u
t
0
.
0
0
9
1
.
9
0
9
1
.
7
9
1
.
9
9
0
.
9
S
M
O
TE
0
.
0
0
9
2
.
0
4
9
3
.
4
9
2
.
0
9
2
.
5
A
D
A
S
Y
N
0
.
0
0
9
2
.
0
4
9
3
.
7
9
2
.
0
9
2
.
5
LR
W
i
t
h
o
u
t
0
.
0
0
9
3
.
7
7
9
3
.
6
9
3
.
8
9
3
.
3
S
M
O
TE
0
.
0
0
9
3
.
6
3
9
5
.
0
9
3
.
6
9
4
.
0
A
D
A
S
Y
N
0
.
0
0
9
4
.
9
6
9
5
.
0
9
5
.
0
9
5
.
0
S
V
M
W
i
t
h
o
u
t
0
.
1
4
9
5
.
0
9
9
5
.
0
9
5
.
1
9
4
.
9
S
M
O
TE
0
.
2
1
9
4
.
9
6
9
5
.
0
9
5
.
0
9
5
.
0
A
D
A
S
Y
N
0
.
2
2
9
5
.
2
3
9
5
.
3
9
5
.
2
9
5
.
2
T
ab
le
3
.
C
o
m
p
a
r
is
o
n
o
f
th
e
co
n
f
u
s
io
n
m
atr
ices d
is
p
lay
in
g
th
e
n
u
m
b
e
r
s
o
f
tr
u
e
p
o
s
itiv
e
(
TP
)
,
T
N,
FP
,
an
d
f
alse n
eg
ativ
e
(
FN
)
D
a
t
a
b
a
l
a
n
c
i
n
g
NB
LR
S
V
M
W
i
t
h
o
u
t
S
M
O
TE
A
D
A
S
Y
N
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.
3
9
,
No
.
1
,
Ju
ly
20
25
:
364
-
3
7
3
370
3
.
3
.
Dis
cus
s
io
n
T
h
is
s
tu
d
y
in
v
esti
g
ated
th
e
ef
f
ec
ts
o
f
d
ata
b
alan
cin
g
tech
n
iq
u
es
o
n
th
e
p
er
f
o
r
m
an
ce
m
etr
ics
o
f
s
en
tim
en
t
an
aly
s
is
u
tili
zin
g
N
B
,
L
R
,
an
d
SVM
alg
o
r
ith
m
s
.
W
h
ile
p
r
io
r
r
esear
ch
h
as
ex
p
l
o
r
ed
th
e
im
p
ac
t
o
f
d
ata
b
alan
cin
g
,
th
e
cu
r
r
e
n
t
in
v
esti
g
atio
n
ad
d
r
ess
es
its
i
n
f
lu
en
ce
w
h
en
th
e
d
ata
e
x
h
ib
its
co
n
s
id
er
ab
le
s
k
ewn
ess
,
as
ev
alu
ated
th
r
o
u
g
h
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
m
etr
ics
in
th
e
co
n
tex
t
o
f
s
en
tim
en
t
an
aly
s
is
.
T
h
e
f
in
d
in
g
s
in
d
ica
te
th
at
th
e
SVM
alg
o
r
ith
m
a
ch
iev
ed
th
e
h
ig
h
est
ac
cu
r
ac
y
with
o
u
t
a
n
y
d
ata
b
alan
cin
g
i
n
ter
v
en
tio
n
,
wh
ile
th
e
ap
p
licatio
n
o
f
ADASYN
im
p
r
o
v
ed
t
h
e
o
v
er
all
d
etec
ti
o
n
o
f
th
e
m
in
o
r
ity
class
.
Alth
o
u
g
h
th
e
im
p
lem
e
n
tatio
n
o
f
SMOT
E
m
ar
g
in
ally
r
ed
u
ce
d
th
e
SVM'
s
o
v
er
all
ac
cu
r
ac
y
t
o
9
4
.
9
6
%
an
d
in
cr
ea
s
ed
th
e
p
r
o
ce
s
s
in
g
tim
e,
it
r
em
ain
ed
th
e
b
est
-
p
er
f
o
r
m
in
g
al
g
o
r
ith
m
f
o
r
s
en
tim
e
n
t
an
aly
s
is
task
s
.
I
n
co
n
tr
ast,
th
e
LR
m
o
d
el
also
ex
p
er
ien
ce
d
a
s
lig
h
t
d
ec
r
ea
s
e
in
ac
cu
r
ac
y
af
ter
th
e
d
ata
b
alan
cin
g
p
r
o
c
ess
.
No
n
eth
eless
,
th
e
NB
an
d
LR
alg
o
r
ith
m
s
o
f
f
er
ed
th
e
f
astes
t tr
ain
in
g
tim
es a
m
o
n
g
th
e
in
v
esti
g
ate
d
m
o
d
els.
T
h
e
cu
r
r
en
t
s
tu
d
y
s
u
g
g
ests
th
at
h
ig
h
er
ac
cu
r
ac
y
d
o
es
n
o
t
in
h
er
en
tly
eq
u
ate
to
p
o
o
r
er
p
er
f
o
r
m
an
ce
in
r
ep
r
esen
tin
g
th
e
m
i
n
o
r
ity
class
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
es
m
ay
g
ai
n
ad
v
an
tag
es
f
r
o
m
d
at
a
b
alan
cin
g
with
o
u
t
s
ig
n
if
ican
tly
co
m
p
r
o
m
is
in
g
th
e
o
v
er
all
ac
cu
r
ac
y
.
T
h
is
f
in
d
i
n
g
alig
n
s
with
ex
is
tin
g
s
ch
o
la
r
ly
liter
atu
r
e
[
3
0
]
,
wh
ich
in
d
icate
s
a
tr
a
d
e
-
o
f
f
b
etwe
en
ac
cu
r
ac
y
an
d
m
in
o
r
ity
class
r
ep
r
esen
tatio
n
in
s
en
t
im
en
t
an
aly
s
is
.
T
h
e
p
r
esen
t
in
v
esti
g
atio
n
ex
am
in
e
d
a
r
a
n
g
e
o
f
alg
o
r
ith
m
s
an
d
b
alan
cin
g
tec
h
n
iq
u
es.
Ho
wev
er
,
f
u
r
th
er
in
-
d
e
p
th
ex
am
in
atio
n
s
m
ay
b
e
n
ec
ess
ar
y
to
c
o
n
f
ir
m
th
e
g
en
er
aliza
b
ilit
y
o
f
th
ese
r
esu
lts
,
p
ar
ticu
l
ar
ly
r
eg
a
r
d
in
g
th
e
in
f
lu
en
ce
o
f
v
ar
y
in
g
le
v
els o
f
im
b
alan
ce
an
d
th
e
u
s
e
o
f
ad
v
a
n
ce
d
n
e
u
r
al
n
etwo
r
k
m
o
d
els.
Ou
r
r
esear
ch
r
ev
ea
ls
t
h
at
d
a
ta
b
alan
c
in
g
m
eth
o
d
s
ar
e
m
o
r
e
r
o
b
u
s
t
th
a
n
f
o
cu
s
in
g
s
o
l
ely
o
n
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
ajo
r
ity
class
.
Fu
tu
r
e
in
v
esti
g
atio
n
s
m
a
y
ex
p
lo
r
e
th
e
co
n
n
ec
tio
n
b
et
wee
n
s
en
tim
en
t
an
d
s
p
ec
if
ic
p
r
o
d
u
ct
ch
ar
ac
te
r
is
tics
,
id
en
tify
in
g
v
iab
le
a
p
p
r
o
ac
h
es
to
g
en
er
atin
g
ac
cu
r
ate
an
d
u
n
b
iased
s
en
tim
en
t
an
aly
s
is
.
R
ec
en
t
o
b
s
er
v
atio
n
s
s
u
g
g
est
th
at
th
e
ch
allen
g
es
p
o
s
ed
b
y
im
b
ala
n
ce
d
d
atasets
in
s
en
tim
en
t
an
aly
s
is
ca
n
lead
to
b
iased
r
esu
lts
an
d
m
is
in
f
o
r
m
ed
b
u
s
in
ess
d
ec
is
io
n
s
[
3
1
]
.
Ou
r
f
in
d
i
n
g
s
d
em
o
n
s
tr
ate
th
at
th
is
p
h
en
o
m
en
o
n
is
ass
o
ciate
d
wit
h
ch
an
g
es
in
p
er
f
o
r
m
a
n
ce
r
ath
er
th
an
s
o
lely
attr
ib
u
tab
le
to
in
cr
ea
s
ed
f
alse
p
o
s
itiv
es o
r
n
eg
ativ
es.
4.
CO
NCLU
SI
O
N
B
ased
o
n
th
e
r
esu
lts
,
we
p
r
o
v
id
ed
c
o
llab
o
r
ated
in
v
esti
g
ati
o
n
s
co
v
e
r
in
g
th
e
m
etr
ics
f
o
r
th
e
th
r
ee
alg
o
r
ith
m
s
with
o
u
t
an
d
with
th
e
d
ata
b
alan
cin
g
p
r
o
ce
s
s
.
W
ith
b
alan
ce
d
d
ata,
all
alg
o
r
ith
m
s
d
em
o
n
s
tr
ated
s
u
b
s
tan
tial
p
er
f
o
r
m
an
ce
im
p
r
o
v
em
en
ts
.
NB
ex
h
ib
ited
th
e
m
o
s
t
s
ig
n
if
ican
t
im
p
r
o
v
em
en
t
s
ac
r
o
s
s
all
m
etr
ics,
with
ac
cu
r
ac
y
in
cr
ea
s
in
g
f
r
o
m
9
1
.
9
1
%
to
9
2
.
0
4
%,
p
r
ec
is
i
o
n
f
r
o
m
9
1
.
7
%
to
9
3
.
7
%,
r
ec
al
l
r
is
in
g
f
r
o
m
9
1
.
9
%
to
9
2
.
0
%,
an
d
th
e
F1
-
s
co
r
e
s
u
b
s
tan
tially
im
p
r
o
v
in
g
f
r
o
m
9
0
.
9
%
to
9
2
.
5
%.
L
R
s
h
o
wed
p
e
r
f
o
r
m
an
ce
en
h
an
ce
m
e
n
ts
,
s
lig
h
tly
in
c
r
ea
s
in
g
th
e
a
cc
u
r
ac
y
f
r
o
m
9
3
.
7
7
%
to
9
4
.
9
6
%.
I
ts
p
r
ec
is
io
n
in
cr
ea
s
ed
m
ar
g
in
ally
f
r
o
m
9
3
.
6
%
to
9
5
.
0
%,
an
d
th
e
F1
-
Sco
r
e
im
p
r
o
v
ed
f
r
o
m
9
3
.
3
%
to
9
5
.
0
%.
SVM
r
em
ain
ed
t
h
e
b
est
-
p
er
f
o
r
m
in
g
alg
o
r
ith
m
f
o
r
s
en
tim
en
t
an
al
y
s
is
with
o
u
t
an
d
with
d
ata
b
alan
cin
g
,
ac
h
iev
i
n
g
th
e
h
ig
h
e
s
t
s
co
r
es
ac
r
o
s
s
all
m
etr
ics.
T
h
e
L
R
an
d
SVM
p
er
f
o
r
m
an
ce
s
h
o
wed
r
elativ
ely
n
eg
ativ
e
tr
en
d
s
u
s
in
g
SMOT
E
.
Ho
w
ev
er
,
in
teg
r
atin
g
ADASYN
in
to
SVM
im
p
r
o
v
ed
t
h
e
o
v
er
all
p
e
r
f
o
r
m
a
n
ce
,
with
ac
cu
r
ac
y
m
a
r
g
in
ally
in
cr
ea
s
in
g
f
r
o
m
9
5
.
0
9
%
to
9
5
.
2
3
%,
p
r
e
cisi
o
n
r
elativ
ely
r
em
ai
n
in
g
s
tab
le
at
9
5
.
3
%,
r
ec
all
g
r
o
win
g
s
o
m
ewh
at
f
r
o
m
9
5
.
1
%
to
9
5
.
2
%,
a
n
d
th
e
F1
-
Sco
r
e
im
p
r
o
v
in
g
f
r
o
m
9
4
.
9
%
to
9
5
.
2
%.
T
h
ese
p
er
f
o
r
m
a
n
ce
im
p
r
o
v
em
e
n
ts
with
SMOT
E
an
d
ADASY
N,
p
ar
ticu
lar
ly
in
F1
-
s
co
r
e
f
o
r
NB
,
L
R
,
an
d
SVM,
in
d
icate
th
at
th
e
d
ata
b
alan
cin
g
p
r
o
ce
s
s
h
as sl
ig
h
tly
en
h
an
ce
d
th
e
u
n
d
e
r
s
tan
d
in
g
a
n
d
p
r
ed
icti
o
n
o
f
m
in
o
r
it
y
class
es.
B
ased
o
n
th
e
p
er
f
o
r
m
an
ce
m
e
tr
ics
o
n
ADASYN,
th
e
m
o
d
els
h
av
e
b
ec
o
m
e
m
o
r
e
r
eliab
le
in
s
en
tim
en
t
an
aly
s
is
,
with
a
m
o
r
e
b
alan
ce
d
p
r
ed
ictio
n
d
is
tr
ib
u
tio
n
ac
r
o
s
s
s
en
tim
en
t
class
es
.
T
h
e
im
p
r
o
v
em
en
ts
ac
r
o
s
s
all
m
etr
ics
d
em
o
n
s
tr
ate
th
at
th
e
b
alan
ce
d
s
am
p
lin
g
o
f
all
class
es
d
u
r
in
g
th
e
lear
n
in
g
p
r
o
ce
s
s
h
as
led
to
m
o
r
e
p
r
ec
is
e
an
d
d
ep
e
n
d
ab
le
s
en
tim
en
t
r
ea
d
in
g
f
r
o
m
th
e
p
r
o
v
id
e
d
d
ata,
b
en
ef
itin
g
in
a
s
u
p
er
io
r
s
en
tim
en
t
an
aly
s
is
ev
alu
atio
n
o
n
ly
f
o
r
NB
.
I
n
v
esti
g
atin
g
n
eu
tr
al
r
ev
i
ews
wo
u
ld
b
e
o
f
in
ter
est,
as
we
h
y
p
o
th
esize
th
at
th
is
d
ir
ec
tio
n
will p
r
esen
t a
b
r
o
ad
er
v
iew
o
f
s
en
tim
en
t a
n
al
y
s
is
o
n
s
k
ewe
d
p
r
o
d
u
ct
r
ev
iews
.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
ac
k
n
o
wled
g
es
t
h
e
s
u
p
p
o
r
t
an
d
ac
ad
em
ic
r
eso
u
r
ce
s
p
r
o
v
id
ed
b
y
th
e
Facu
lty
o
f
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
an
d
Data
Scien
ce
,
Un
iv
er
s
itas
Se
b
elas
Ma
r
et,
wh
ich
f
ac
ilit
ated
th
e
co
m
p
letio
n
o
f
th
is
r
esear
ch
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
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
A
s
en
timen
t a
n
a
lysi
s
o
n
s
ke
w
ed
p
r
o
d
u
ct
r
ev
iew
s
:
B
en
&
Je
r
r
y'
s
ice
crea
m
(
N
a
b
illa
N
u
r
u
l
ita
Dewi
)
371
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
e
r
esear
ch
p
r
o
ject
in
v
o
lv
e
d
m
u
ltip
le
co
n
tr
ib
u
to
r
s
with
v
ar
ied
r
esp
o
n
s
ib
ilit
ies.
Nab
illa
Nu
r
u
lita
Dew
i
was
ex
ten
s
iv
ely
in
v
o
l
v
ed
ac
r
o
s
s
m
o
s
t
asp
ec
ts
,
co
n
tr
ib
u
tin
g
to
c
o
n
ce
p
t
u
aliza
tio
n
,
m
eth
o
d
o
lo
g
y
,
s
o
f
twar
e,
f
o
r
m
al
an
aly
s
is
,
in
v
esti
g
atio
n
,
r
eso
u
r
ce
s
,
d
ata
cu
r
atio
n
,
o
r
i
g
in
al
d
r
af
tin
g
,
ed
itin
g
,
an
d
v
is
u
aliza
tio
n
.
Sek
ar
Gesti Am
alia
Utam
i f
o
cu
s
ed
o
n
co
n
ce
p
tu
aliza
tio
n
,
m
e
th
o
d
o
lo
g
y
,
a
n
d
s
o
f
twar
e
d
ev
el
o
p
m
en
t.
Sh
alsab
ila
Au
r
a
Ad
iar
h
ad
a
s
ig
n
if
ican
t
r
o
le,
p
ar
ticip
atin
g
in
c
o
n
ce
p
t
u
a
lizatio
n
,
m
eth
o
d
o
lo
g
y
,
s
o
f
twa
r
e,
f
o
r
m
al
an
al
y
s
is
,
in
v
esti
g
atio
n
,
r
eso
u
r
ce
s
,
d
ata
cu
r
atio
n
,
o
r
ig
i
n
al
d
r
af
tin
g
,
ed
itin
g
,
an
d
v
is
u
aliza
tio
n
.
H
asan
Dwi
C
ah
y
o
n
o
co
n
tr
ib
u
ted
to
m
eth
o
d
o
lo
g
y
,
v
alid
atio
n
,
e
d
itin
g
,
p
r
o
ject
ad
m
in
is
tr
atio
n
,
an
d
f
u
n
d
i
n
g
ac
q
u
is
itio
n
,
s
u
g
g
esti
n
g
a
s
u
p
er
v
is
o
r
y
o
r
ad
m
in
is
tr
ativ
e
r
o
le
in
th
e
r
esear
ch
team
,
with
p
ar
ticu
lar
em
p
h
asis
o
n
s
ec
u
r
in
g
f
in
an
cial
r
eso
u
r
ce
s
f
o
r
t
h
e
p
r
o
ject.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Nab
illa N
u
r
u
lita D
ewi
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
Sek
ar
Gesti Am
alia
Utam
i
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
Sh
alsab
ila
Au
r
a
Ad
iar
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
Hasan
Dwi
C
ah
y
o
n
o
✔
✔
✔
✔
✔
✔
✔
✔
✔
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
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Evaluation Warning : The document was created with Spire.PDF for Python.