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17
,
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.
2
,
Feb
r
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lled
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1
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in
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c
o
n
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is
ts
o
f
f
i
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elem
en
ts
[
1
]
.
a)
E
x
tr
ac
t,
tr
an
s
f
o
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m
an
d
lo
ad
d
e
alin
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m
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ly
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d
wh
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g
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est.
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al
wo
r
k
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s
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n
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r
p
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ce
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b
e
M.
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Oso
f
is
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A.
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an
d
W
illi
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W
.
F
[
2
]
h
av
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d
esig
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Ar
tific
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k
(
ANN)
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is
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r
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ch
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e
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y
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tem
W
a
s
tes
ted
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s
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ata
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r
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m
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“Nig
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ian
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s
ity
”.
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en
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k
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r
awa
l1
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J
y
o
ti
Sin
g
h
,
an
d
Z
ad
g
o
an
k
ar
[
3
]
“su
g
g
ests
ty
p
e
to
esti
m
ate
th
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p
er
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o
r
m
a
n
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ata
m
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lik
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(
“a
s
s
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class
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Dec
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ee
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K
-
N
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ïv
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esian
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to
s
ea
r
ch
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to
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is
t
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u
p
p
ly
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ca
tio
n
al
p
r
o
ce
s
s
”.
Hem
aid
an
d
E
l
-
Hale
es
[
4
]
i
n
th
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s
tu
d
y
th
ey
u
s
e
Qu
esti
o
n
n
air
e
wh
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as q
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th
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th
ey
in
ten
d
a
f
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r
m
to
"test
teac
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p
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f
o
r
m
an
ce
d
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r
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g
d
ata
m
in
in
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tec
h
n
iq
u
es
lik
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,
class
if
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,
ass
o
ciatio
n
r
u
les
to
f
i
n
d
o
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t
b
eh
av
io
r
to
aid
t
h
em
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o
war
d
en
h
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d
th
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I
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J
E
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E
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&
C
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m
p
Sci,
Vo
l.
17
,
No
.
2
,
Feb
r
u
a
r
y
20
20
:
1
0
2
9
-
1
0
3
9
1030
lear
n
in
g
p
r
o
ce
d
u
r
e
an
d
im
p
r
o
v
e
th
e
p
r
esen
tatio
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o
f
teac
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er
s
in
class
r
o
o
m
”,
to
en
h
a
n
ce
th
e
ed
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ca
tio
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r
o
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an
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ex
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co
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tr
ib
u
tio
n
o
f
teac
h
er
s
in
th
e
cla
s
s
r
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o
m
”.
W
o
r
k
s
p
u
b
lis
h
ed
b
y
Ah
m
ed
a,
R
izan
er
c
an
d
Ulu
s
o
y
c
[
5
]
u
s
in
g
T
h
e
s
eq
u
en
tial
Min
i
m
al
Op
tim
i
za
tio
n
,
Naïv
e
B
ay
es,
J
4
8
D
ec
is
io
n
T
r
ee
,
an
d
Mu
ltil
ay
er
Per
ce
p
tio
n
to
E
v
al
u
ate
Stu
d
en
t
r
ec
o
r
d
s
to
p
r
ed
ic
t
th
e
teac
h
er
p
er
f
o
r
m
a
n
ce
an
d
in
v
esti
g
ates
f
ac
to
r
s
th
at
h
av
e
af
f
ec
ted
s
tu
d
en
ts
a
ch
iev
em
en
ts
to
d
ev
el
o
p
t
h
e
t
ea
ch
in
g
s
y
s
tem
,
.
I
n
[
6
]
,
in
h
is
s
tu
d
y
to
Pre
d
ict
s
tu
d
en
ts
'
p
er
f
o
r
m
an
ce
h
e
f
in
d
s
th
at
th
e
class
if
icatio
n
s
ch
em
e
is
r
ep
ea
te
d
ly
u
s
ed
i
n
ed
u
ca
tio
n
al
d
ata
m
in
in
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ar
ea
,
it
in
clu
d
es,
Neu
r
alNe
two
r
k
an
d
Dec
is
io
n
T
r
ee
,
th
e
t
wo
m
eth
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d
s
g
r
ea
tly
u
s
ed
b
y
th
e
r
esear
ch
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s
f
o
r
p
r
ed
ictin
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s
tu
d
en
ts
'
p
er
f
o
r
m
a
n
ce
.
Ms.
A.
Pav
ith
r
a,
Mr
.
S.
Dh
an
ar
aj
[
7
]
I
n
th
eir
s
tu
d
y
th
ey
ex
am
in
e
th
e
p
r
ed
ictio
n
ac
c
u
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ess
o
f
th
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ac
ad
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ic
p
er
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o
r
m
a
n
ce
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te
ac
h
in
g
th
e
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tu
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n
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s
in
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d
if
f
er
en
t
class
if
icatio
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alg
o
r
ith
m
s
lik
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“M
L
P,
Naïv
e
B
ay
es,
Dec
i
s
io
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tr
ee
,
R
E
P
tr
ee
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an
d
J
4
8
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,
th
ey
co
n
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d
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th
at
“m
an
y
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ac
to
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lu
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tu
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en
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ay
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ts
".
Far
id
J
au
h
ar
i,
Ah
m
a
d
Af
if
Su
p
ian
t
o
[
8
]
p
r
o
p
o
s
es
th
r
ee
b
o
o
s
tin
g
a
lg
o
r
ith
m
s
(
C
5
.
0
,
Ad
aBo
o
s
t.
M1
,
an
d
Ad
aBo
o
s
t.
SAMM
E
)
to
b
u
ild
t
h
e
clas
s
if
ier
f
o
r
p
r
e
d
ictin
g
s
tu
d
en
t'
s
p
er
f
o
r
m
an
ce
.
T
h
e
y
u
s
ed
t
h
r
ee
s
ce
n
ar
i
o
s
o
f
ev
alu
atio
n
,
th
e
f
ir
s
t
s
ce
n
ar
io
em
p
lo
y
s
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
to
co
m
p
ar
e
t
h
e
p
er
f
o
r
m
an
ce
o
f
b
o
o
s
tin
g
alg
o
r
ith
m
s
.
T
h
e
s
ec
o
n
d
s
ce
n
ar
io
was
ac
cu
s
to
m
ed
to
ev
alu
at
e
b
o
o
s
tin
g
alg
o
r
ith
m
s
b
elo
w
t
h
e
v
a
r
io
u
s
v
ar
ieties
o
f
co
ac
h
i
n
g
in
f
o
r
m
atio
n
with
in
th
e
th
ir
d
s
ce
n
a
r
io
,
th
e
y
b
u
il
d
m
o
d
els
f
r
o
m
o
n
e
s
u
b
ject
Da
taset,
an
d
test
u
s
in
g
an
o
th
er
s
u
b
ject
Data
s
et.
T
h
e
y
co
n
clu
d
e
th
at
th
e
th
ir
d
s
ce
n
ar
i
o
r
esu
lts
in
d
icate
th
at
th
e
y
ca
n
b
u
ild
a
p
r
ed
ictio
n
m
o
d
el
u
s
in
g
o
n
e
s
u
b
ject
to
p
r
ed
ict
an
o
th
er
.
B
in
Ma
t
an
d
N.
B
u
n
iy
am
in
[
9
]
u
s
in
g
n
e
u
r
o
-
f
u
zz
y
to
o
l
to
class
if
y
an
d
p
r
ed
ict
elec
tr
ical
en
g
in
e
er
in
g
s
tu
d
en
ts
g
r
ad
u
atio
n
ac
h
iev
em
en
t
b
ased
o
n
m
ath
em
atics
co
m
p
eten
cy
.
I
t'
s
s
u
p
p
o
r
ted
lo
n
g
itu
d
in
al
p
r
o
g
r
ess
an
d
cr
o
s
s
-
v
alid
atio
n
m
o
d
el
o
n
two
ar
ith
m
etic
s
u
b
jects,
s
em
ester
s
’
p
er
f
o
r
m
an
ce
,
a
n
d
g
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d
u
atio
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ac
h
iev
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o
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tr
ical
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tu
d
en
ts
.
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h
ey
co
n
cl
u
d
e
th
at
th
e
m
ix
tu
r
e
o
f
s
tatis
tical
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o
ciate
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aly
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d
m
ac
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ac
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tr
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ata,
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alter
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ity
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lo
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at
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h
ey
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o
p
ed
th
at
th
eir
f
in
d
in
g
s
ca
n
h
elp
f
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lty
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a
n
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em
en
t
to
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ev
iew
m
ath
em
atics c
u
r
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icu
lu
m
with
r
esp
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t in
th
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cr
ea
s
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g
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o
f
en
g
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ee
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in
g
f
ield
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Hu
s
s
ain
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A.
Dah
an
,
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M.
B
a
-
Alwib
,
an
d
N.
R
ib
ata
[
1
0
]
u
s
ed
f
o
r
class
if
icatio
n
s
m
eth
o
d
s
,
(
J
4
8
,
PAR
T
,
R
an
d
o
m
Fo
r
est
an
d
B
ay
es
Netwo
r
k
C
lass
if
ier
s
)
.
T
h
e
h
ig
h
in
f
lu
en
tial
attr
ib
u
tes
wer
e
s
elec
ted
u
s
in
g
th
e
d
ata
m
in
in
g
to
o
l
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ek
a.
T
h
ey
co
n
clu
d
e
t
h
at
th
e
R
an
d
o
m
Fo
r
est C
la
s
s
if
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n
m
eth
o
d
was th
e
m
o
s
t su
ited
alg
o
r
ith
m
f
o
r
th
e
Data
s
et.
Sao
u
ab
i
Mo
h
am
e
d
,
Ab
d
u
lla
h
E
zz
ati
[
1
1
]
p
r
o
p
o
s
es
a
d
ata
m
i
n
in
g
p
r
o
ce
s
s
f
o
r
em
p
lo
y
a
b
ilit
y
d
ata
u
s
in
g
class
if
icatio
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tech
n
iq
u
es
(
th
e
Dec
is
io
n
T
r
ee
class
if
ier
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o
g
is
tic
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eg
r
ess
io
n
,
an
d
Naïv
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B
ay
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o
r
ith
m
s
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ap
p
ly
th
em
b
y
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a
p
id
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d
io
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tio
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Ver
s
io
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1
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0
0
0
)
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s
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g
e
m
p
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et.
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th
at
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class
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ier
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e
ac
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ate
th
an
L
o
g
is
tic
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eg
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an
d
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ay
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2.
1
.
P
re
pa
re
Da
t
a
F
o
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th
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aly
s
is
,
d
ata
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o
llected
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tu
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e
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a
r
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ts
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e
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lty
o
f
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lleg
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o
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n
f
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m
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n
g
in
ee
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in
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OI
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)
at
Al
Nah
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s
ity
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o
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;
h
o
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illed
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p
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o
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g
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ter
m
.
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h
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in
f
o
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llected
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ty
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to
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d
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teac
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p
e
r
f
o
r
m
an
ce
as sh
o
wn
in
T
ab
le
1
.
a)
Pre
p
ar
in
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n
f
o
r
m
atio
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,
T
ea
ch
er
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s
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wa
s
E
v
alu
ated
.
Attr
ib
u
tes an
d
v
al
u
e
wer
e
d
eter
m
in
ed
.
b)
Dat
a
s
av
ed
in
ex
ce
l f
ile
in
C
SV (
C
o
m
m
a
Sep
ar
ated
Valu
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c)
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o
u
s
e
W
ek
a
d
ata
m
u
s
t b
e
c
o
n
v
er
ted
to
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r
f
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ib
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r
m
at)
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Usi
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g
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ek
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(
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h
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d
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lo
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r
.
e)
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o
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an
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en
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f
f
f
ile.
f)
Ap
p
ly
class
if
icatio
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alg
o
r
ith
m
s
(
Z
er
o
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,
SMO,
Naïv
e
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ay
esi
an
,
J
4
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tr
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a
n
d
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an
d
o
m
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r
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t)
.
g)
E
v
alu
ate
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e
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lt a
n
d
p
er
f
o
r
m
an
ce
.
Fig
u
r
e
1
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8
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9
TB
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o
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R
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b
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c
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p
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t
10
C
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LA
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c
o
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b
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p
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d
d
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f
f
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t
st
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s.
11
Q
U
I
Z.
A
S
S
.
P
R
O
J.E
X
A
.
_
H
EP.
Th
e
q
u
i
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a
ssi
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me
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ms c
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l
.
12
EN
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13
I
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14
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16
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17
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18
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19
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20
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21
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23
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20
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0
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3
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R
[
1
2
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lick
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ty
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Feb
r
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r
y
20
20
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ee
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r
th
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7
6
m
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ter
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ly
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e
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.
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F
ig
u
r
e
1
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
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E
n
g
&
C
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m
p
Sci
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N:
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4
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2
A
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Fig
u
r
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.
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class
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u
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t
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r
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B
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din
g
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he
Ra
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s
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M
o
dels
[
1
2
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T
h
e
class
if
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o
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tp
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t u
s
ed
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o
r
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d
o
m
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ith
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r
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h
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r
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ed
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r
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9
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1
it'
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ter
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im
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ly
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e
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er
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o
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m
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As
in
F
ig
u
r
e
1
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
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4
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2
I
n
d
o
n
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J
E
lec
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n
g
&
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m
p
Sci,
Vo
l.
17
,
No
.
2
,
Feb
r
u
a
r
y
20
20
:
1
0
2
9
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1
0
3
9
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lass
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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J
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lec
E
n
g
&
C
o
m
p
Sci
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SS
N:
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4
7
5
2
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n
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la
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tio
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n
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R
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ma
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3.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
I
O
N
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.
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A
t
t
ribute
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nk
ing
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1
6
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20]
W
ek
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p
lo
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alu
ate
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h
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ata
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y
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2
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m
a
n
ce
s
o
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th
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5
m
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ar
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r
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alg
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s
:
[
1
1
,
23
-
25]
T
P=tr
u
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p
o
s
itiv
es": a
v
ar
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ex
am
p
les": Pr
ed
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n
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T
N=
tr
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v
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x
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les ":
p
r
ed
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n
eg
a
tiv
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e
liter
ally
n
eg
ati
v
e.
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.
17
,
No
.
2
,
Feb
r
u
a
r
y
20
20
:
1
0
2
9
-
1
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3
9
1038
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Ma
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u
an
tity
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f
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s
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s
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at
t
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e
to
tal
o
f
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in
th
e
m
at
r
ix
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l
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it in
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P FN
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R
a
n
d
o
mF
o
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e
st
4.
CO
NCLU
SI
O
N
Fro
m
th
e
r
esu
lt
o
f
c
o
m
p
ar
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n
o
f
t
h
e
f
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alg
o
r
ith
m
s
a
s
in
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les
4
an
d
5
it
c
o
n
clu
d
e
th
at
Alg
o
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ith
m
s
SMO
an
d
R
an
d
o
m
f
o
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est
p
r
ed
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ig
h
er
th
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n
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ativ
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alg
o
r
ith
m
s
s
in
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r
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at
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ig
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est
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n
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ith
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m
a
n
ce
,
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tes
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e
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ed
,
a
n
d
f
o
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n
d
th
at
a
f
ew
o
f
th
em
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e
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ec
tiv
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o
n
th
e
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er
f
o
r
m
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ce
p
r
ed
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n
.
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h
e
teac
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er
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d
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ted
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e
u
s
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l
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d
en
t
s
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was
th
e
s
tr
o
n
g
est
attr
ib
u
te
an
d
th
en
th
e
r
esu
lt
p
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s
a
v
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r
o
le
with
in
th
e
p
e
r
f
o
r
m
a
n
ce
o
f
ac
a
d
em
ics.
Mo
r
e
a
lo
t
o
f
r
em
o
v
in
g
th
e
wo
r
s
t
h
ier
ar
ch
al
attr
ib
u
tes
(
1
0
,
1
1
,
1
2
,
a
n
d
1
4
)
,
th
at
h
av
e
a
lo
wer
im
p
ac
t
o
n
th
e
d
ataset
ca
n
in
cr
ea
s
e
th
e
alg
o
r
ith
m
s
p
er
f
o
r
m
a
n
ce
ac
cu
r
ac
ies.
RE
F
E
R
E
NC
E
S
[1
]
S
h
a
fiq
As
lam
,
Im
ra
n
As
h
ra
f,
“
Da
ta
M
in
in
g
Alg
o
rit
h
m
s
a
n
d
Th
e
ir
Ap
p
li
c
a
ti
o
n
s
i
n
E
d
u
c
a
ti
o
n
Da
ta
M
in
i
n
g
”
,
In
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
Res
e
a
rc
h
i
n
C
o
mp
u
ter
S
c
ien
c
e
a
n
d
M
a
n
a
g
e
me
n
t
S
t
u
d
ies
,
Vo
l
2
,
I
ss
u
e
7
,
p
g
.
5
0
-
56
,
J
u
ly
2
0
1
4
.
[2
]
As
a
n
b
e
M
.
O.
,
Os
o
fisa
n
A.O.,
Wi
ll
iam
W.
F
.
“
Tea
c
h
e
rs’
P
e
rfo
rm
a
n
c
e
Ev
a
lu
a
ti
o
n
i
n
Hig
h
e
r
E
d
u
c
a
ti
o
n
a
l
In
stit
u
t
i
o
n
u
sin
g
Da
ta
M
i
n
i
n
g
Tec
h
n
iq
u
e
”
,
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ap
p
li
e
d
I
n
fo
rm
a
t
io
n
S
y
ste
ms
(IJ
AIS
)
–
IS
S
N:
2
2
4
9
-
0
8
6
8
F
o
u
n
d
a
ti
o
n
o
f
C
o
m
p
u
ter S
c
ien
c
e
F
CS
,
Ne
w Yo
r
k
,
US
A V
o
lu
m
e
1
0
–
N
o
.
7
,
M
a
rc
h
2
0
1
6
.
[3
]
Re
n
u
k
a
Ag
ra
wa
l1
,
Jy
o
ti
S
i
n
g
h
,
A
.
S
.
Zad
g
o
a
n
k
a
r,
“
S
u
m
m
a
ti
v
e
As
se
ss
m
e
n
t
fo
r
P
e
rfo
rm
a
n
c
e
Ev
a
lu
a
ti
o
n
o
f
a
F
a
c
u
lt
y
Us
in
g
D
a
ta
M
in
in
g
Tec
h
n
i
q
u
e
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
in
Co
mp
u
ter
a
n
d
Co
mm
u
n
ica
ti
o
n
En
g
i
n
e
e
rin
g
I
S
O 3
2
9
7
:
2
0
0
7
Ce
rti
fied
Vo
l.
5
,
Iss
u
e
1
0
,
Oc
to
b
e
r
2
0
1
6
.
[4
]
Ra
n
d
a
Kh
.
He
m
a
id
1
,
Ala
a
M
.
El
-
Ha
lee
s,
“
Im
p
ro
v
i
n
g
Tea
c
h
e
r
P
e
rfo
rm
a
n
c
e
u
sin
g
Da
ta
M
i
n
in
g
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
Res
e
a
rc
h
in
Co
mp
u
ter
a
n
d
Co
mm
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
V
o
l.
4
,
Iss
u
e
2
,
F
e
b
ru
a
ry
2
0
1
5
.
[5
]
Ah
m
e
d
M
o
h
a
m
e
d
Ah
m
e
d
a
,
Ah
m
e
t
Riza
n
e
rc
,
Ali
H
a
k
a
n
Ulu
so
y
c
,
“
Us
in
g
D
a
ta
M
in
i
n
g
t
o
Pr
e
d
ict
In
stru
c
to
r
Per
fo
rm
a
n
c
e
”
,
1
2
th
In
tern
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
o
n
A
p
p
l
ica
ti
o
n
o
f
F
u
z
z
y
S
y
st
e
m
s
a
n
d
S
o
ft
Co
m
p
u
ti
n
g
,
ICAF
S
,
Vie
n
n
a
,
Au
stria.
29
-
3
0
Au
g
u
st
2
0
1
6
.
[6
]
A.
M
o
h
a
m
e
d
S
h
a
h
iri
a
,
W
.
Hu
sa
in
a
,
N.
Ab
d
u
l
Ra
sh
id
a
,
“
A
Rev
iew o
n
Pre
d
icti
n
g
S
t
u
d
e
n
t’s
Per
f
o
rm
a
n
c
e
u
sin
g
D
a
ta
M
in
i
n
g
T
e
c
h
n
iq
u
e
s”
P
r
o
c
e
d
ia
Co
m
p
u
ter
S
c
ien
c
e
7
2
,
4
1
4
–
4
2
2
,
EL
S
EVIE
R
2
0
1
5
.
A
v
a
il
a
b
le
o
n
li
n
e
a
t
ww
w.sc
ien
c
e
d
irec
t.
c
o
m
[7
]
M
s.A.P
a
v
it
h
ra
,
M
r.
S
.
Dh
a
n
a
ra
j,
“
P
re
d
ictio
n
Ac
c
u
ra
c
y
o
n
Ac
a
d
e
m
ic
P
e
rfo
rm
a
n
c
e
o
f
S
tu
d
e
n
ts
Us
in
g
Di
ffe
re
n
t
Da
ta
M
in
i
n
g
Al
g
o
r
it
h
m
s wit
h
In
f
lu
e
n
c
i
n
g
F
a
c
to
rs”
,
IJ
S
RC
S
AM
S
Vo
l
7
,
I
ss
u
e
5
,
2
0
1
8
.
[8
]
F
a
rid
Ja
u
h
a
ri
,
Ah
m
a
d
Afif
S
u
p
i
a
n
to
,
“
Bu
il
d
in
g
S
tu
d
e
n
t’s
P
e
rf
o
r
m
a
n
c
e
De
c
isio
n
Tree
Clas
sifier
Us
in
g
B
o
o
sti
n
g
Alg
o
rit
h
m
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(IJ
EE
CS
)
V
o
l.
1
4
,
No
.
3
,
p
p
.
1
2
9
8
-
1
3
0
4
,
J
u
n
e
2
0
1
9
.
[9
]
U.
Bin
M
a
t
a
n
d
N.
Bu
n
iy
a
m
in
,
“
Us
in
g
Ne
u
ro
-
F
u
z
z
y
Tec
h
n
i
q
u
e
to
Clas
sify
a
n
d
P
re
d
ict
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
S
tu
d
e
n
tsAc
h
iev
e
m
e
n
t
Up
o
n
G
ra
d
u
a
ti
o
n
Ba
se
d
On
M
a
th
e
m
a
ti
c
s
Co
m
p
e
ten
c
y
,
”
I
n
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(IJ
EE
CS
)
.
,
v
o
l.
5
,
n
o
.
3
,
p
p
.
6
8
4
–
6
9
0
,
2
0
1
7
.
[1
0
]
S
.
Hu
ss
a
in
,
N.
A.
Da
h
a
n
,
F
.
M
.
Ba
-
Alwib
,
a
n
d
N.
Rib
a
ta,
“
E
d
u
c
a
ti
o
n
a
l
d
a
ta
m
in
in
g
a
n
d
a
n
a
ly
sis
o
f
stu
d
e
n
ts
a
c
a
d
e
m
ic
p
e
rfo
rm
a
n
c
e
u
sin
g
W
EKA,”
I
n
d
o
n
e
sia
n
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(IJ
EE
CS
)
.
,
v
o
l.
9
,
n
o
.
2
,
p
p
.
4
4
7
–
4
5
9
,
2
0
1
8
.
[1
1
]
S
a
o
u
a
b
i
M
o
h
a
m
e
d
,
Ab
d
u
ll
a
h
E
z
z
a
ti
“
A
d
a
ta
m
in
in
g
p
ro
c
e
ss
u
sin
g
c
las
sifica
ti
o
n
tec
h
n
iq
u
e
s
fo
r
e
m
p
lo
y
a
b
i
li
ty
p
re
d
ictio
n
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
t
ric
a
l
En
g
in
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(IJ
EE
CS
)
Vo
l.
1
4
,
No
.
2
,
p
p
.
1
0
2
5
-
1
0
2
9
,
M
a
y
2
0
1
9
,
[1
2
]
Tu
to
r
ialsp
o
i
n
t.
c
o
m
,
“
Da
t
a
M
in
i
n
g
T
u
to
ri
a
l
S
imp
ly E
a
sy
L
e
a
rn
in
g
,
”
2
-
11
-
2
0
1
4
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