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C
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Un
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Ph
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titu
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’
m
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[
1
]
.
Facto
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f
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u
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[
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.
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[
3
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,
[
4
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.
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Stu
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
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52
In
d
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J
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&
C
o
m
p
Sci
,
Vo
l.
3
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No
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1
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J
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ar
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20
2
5
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f
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lin
k
s
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an
d
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ca
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im
p
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f
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5
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6
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[
7
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d
if
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am
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m
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(
Mo
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,
B
lack
b
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C
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v
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iSp
r
in
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L
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n
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Sak
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d
Go
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C
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Mo
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8
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Mo
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9
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1
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1
1
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Mo
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s
o
n
alize
d
lear
n
in
g
.
Mo
o
d
le
h
as
s
h
o
wn
p
o
s
itiv
e
r
esu
lts
in
en
h
a
n
cin
g
th
e
teac
h
in
g
an
d
lear
n
in
g
p
r
o
ce
s
s
o
f
in
s
titu
tio
n
s
u
s
in
g
it
[
1
2
]
.
W
ith
Mo
o
d
le,
e
d
u
ca
tio
n
al
d
ata
m
in
in
g
(
E
DM
)
ca
n
b
e
en
h
an
ce
d
.
E
d
u
ca
t
o
r
s
an
d
ad
m
in
is
tr
ato
r
s
ca
n
u
s
e
Mo
o
d
le
lo
g
s
to
ex
tr
ac
t
d
ata
s
u
m
m
ar
ies
an
d
v
is
u
aliza
tio
n
s
,
g
en
er
atin
g
r
elev
an
t
in
s
ig
h
ts
o
n
h
o
w
s
tu
d
e
n
ts
in
ter
ac
t
with
th
e
L
MS.
E
DM
p
r
o
v
id
es
tech
n
iq
u
es
f
o
r
p
r
ed
ictin
g
s
tu
d
en
ts
at
r
is
k
o
f
f
ailin
g
a
co
u
r
s
e.
T
h
r
o
u
g
h
E
DM
,
Mo
o
d
le
d
ata
ca
n
b
e
an
aly
ze
d
to
id
en
tify
lo
w
-
p
er
f
o
r
m
i
n
g
s
tu
d
en
ts
an
d
th
e
m
o
s
t
in
f
lu
en
cin
g
f
ac
to
r
s
o
f
ac
ad
em
i
c
p
er
f
o
r
m
an
ce
,
ai
d
in
g
in
ea
r
ly
in
ter
v
en
tio
n
s
[
1
3
]
.
E
DM
u
s
es
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
to
p
r
e
d
ict
s
tu
d
en
t
p
er
f
o
r
m
an
ce
an
d
p
r
o
v
id
e
i
n
s
ig
h
ts
f
o
r
ed
u
ca
to
r
s
a
n
d
a
d
m
in
is
tr
ato
r
s
,
g
iv
in
g
th
em
o
p
p
o
r
tu
n
ities
f
o
r
ea
r
ly
in
ter
v
en
tio
n
s
[
1
4
]
.
T
h
e
a
p
p
licatio
n
o
f
E
D
M
s
u
p
p
o
r
ts
in
f
o
r
m
ed
d
ec
is
io
n
-
m
ak
in
g
an
d
im
p
r
o
v
es
ed
u
ca
tio
n
al
o
u
tco
m
es
[
1
5
]
.
B
y
a
n
aly
zin
g
Mo
o
d
le
lo
g
s
an
d
u
s
in
g
v
a
r
io
u
s
m
ac
h
in
e
lear
n
in
g
an
d
o
th
er
d
ata
a
n
aly
s
is
tech
n
iq
u
es,
E
DM
im
p
r
o
v
es
th
e
r
el
iab
ilit
y
o
f
ac
ad
em
ic
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
s
.
Ma
c
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
s
u
ch
as
r
an
d
o
m
f
o
r
ests
(
R
F)
an
d
d
ec
is
io
n
tr
ee
s
(
DT
)
,
ar
e
co
m
m
o
n
l
y
u
s
ed
in
E
DM
to
ac
h
iev
e
r
eliab
le
r
esu
lts
.
Ad
d
itio
n
ally
,
E
DM
co
n
tr
ib
u
tes to
d
ev
elo
p
in
g
s
y
s
tem
s
th
at
u
n
d
e
r
s
tan
d
s
tu
d
en
t
in
ter
a
ctio
n
s
an
d
lear
n
in
g
with
in
o
n
lin
e
en
v
ir
o
n
m
en
ts
[
1
6
]
.
Ov
er
all,
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
tech
n
i
q
u
es
en
h
an
ce
s
th
e
ab
ilit
y
to
p
r
ed
ict
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
,
o
f
f
er
in
g
o
p
p
o
r
t
u
n
ities
f
o
r
in
ter
v
en
tio
n
.
T
h
is
s
tr
ateg
y
ca
n
b
en
ef
it b
o
th
s
tu
d
en
ts
an
d
th
e
i
n
s
titu
tio
n
.
Mu
ltip
le
s
tu
d
ies
[
1
7
]
-
[
2
4
]
h
av
e
u
s
ed
E
DM
to
p
r
e
d
ict
s
tu
d
en
t
p
er
f
o
r
m
an
ce
u
s
in
g
M
o
o
d
le
l
o
g
s
.
Ho
wev
er
,
s
tu
d
ies
th
at
u
s
ed
m
ac
h
in
e
lear
n
in
g
f
o
cu
s
ed
m
o
r
e
o
n
in
c
r
ea
s
in
g
m
o
d
el
p
e
r
f
o
r
m
an
ce
in
th
e
tr
ain
in
g
an
d
test
in
g
en
v
ir
o
n
m
e
n
ts
with
o
u
t
co
n
s
id
er
in
g
g
e
n
er
aliza
b
il
ity
.
Oth
er
s
tu
d
ies
u
s
ed
o
n
ly
q
u
an
titativ
e
an
aly
s
is
.
T
h
ese
p
r
ev
io
u
s
s
tu
d
ies
u
s
ed
Mo
o
d
le
d
ata
f
r
o
m
o
n
ly
a
s
in
g
le
to
a
f
ew
co
u
r
s
es
o
r
co
u
r
s
es
with
th
e
s
am
e
in
s
tr
u
cto
r
with
o
u
t
co
n
s
id
er
in
g
h
eter
o
g
en
eit
y
.
T
h
is
co
n
ce
r
n
ca
n
n
eg
ativ
ely
im
p
ac
t
f
in
d
i
n
g
s
wh
en
ap
p
lied
to
o
th
er
s
ettin
g
s
.
Fu
r
th
er
m
o
r
e,
t
h
ese
s
tu
d
ies
d
if
f
e
r
in
th
e
f
ea
t
u
r
es
u
s
ed
an
d
m
ay
n
o
t
b
e
M
o
o
d
le
g
en
er
ic;
s
o
m
e
m
ay
n
o
t a
p
p
ly
to
o
t
h
er
s
ettin
g
s
.
Ov
er
all,
th
e
p
r
ev
io
u
s
s
tu
d
ies we
r
e
lim
ited
in
ter
m
s
o
f
th
e
tr
ain
in
g
d
ata,
wh
ich
ca
n
n
eg
ativ
ely
im
p
ac
t g
e
n
er
ali
za
b
ilit
y
[
2
5
]
.
Gen
er
aliza
b
ilit
y
r
em
ain
s
an
i
s
s
u
e
in
m
ac
h
in
e
lear
n
in
g
,
wh
er
e
m
o
d
els
p
er
f
o
r
m
p
o
o
r
ly
o
n
n
ew
d
ata
[
2
6
]
-
[
2
8
]
.
Gen
e
r
aliza
b
ilit
y
en
s
u
r
es
m
o
d
el
d
ep
lo
y
a
b
ilit
y
to
o
th
er
Mo
o
d
le
co
u
r
s
es.
T
h
er
e
f
o
r
e,
th
is
s
tu
d
y
aim
e
d
to
p
r
ed
ict
s
tu
d
e
n
ts
’
p
er
f
o
r
m
a
n
ce
u
s
in
g
Mo
o
d
le
lo
g
s
with
m
ac
h
in
e
lear
n
i
n
g
u
s
in
g
g
en
e
r
alize
d
f
ea
tu
r
es
an
d
m
u
ltip
le
co
u
r
s
e
d
ata,
en
ab
lin
g
m
o
d
el
d
e
p
lo
y
a
b
ilit
y
to
o
th
e
r
Mo
o
d
le
co
u
r
s
es.
T
h
e
s
tu
d
y
f
o
cu
s
ed
o
n
d
ev
el
o
p
in
g
a
p
r
e
d
ictiv
e
m
o
d
el
b
y
co
m
p
a
r
in
g
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
tr
ain
e
d
o
n
m
u
l
tip
l
e
Mo
o
d
le
co
u
r
s
e
d
ata
with
g
en
er
ic
f
ea
tu
r
es,
aim
in
g
to
b
e
ap
p
lied
t
o
v
a
r
io
u
s
co
u
r
s
es.
T
h
e
s
tu
d
y
also
aim
e
d
at
id
e
n
tify
in
g
th
e
f
ac
to
r
s
(
f
ea
tu
r
es)
th
at
c
o
n
tr
ib
u
te
to
ac
a
d
em
ic
p
er
f
o
r
m
a
n
ce
.
I
n
s
titu
tio
n
s
ca
n
u
s
e
th
e
s
tu
d
y
’
s
o
u
tco
m
e
as
a
p
r
ed
ictiv
e
m
o
d
el
to
i
n
f
o
r
m
ac
ad
em
ic
-
r
e
lated
d
ec
is
io
n
s
an
d
f
u
tu
r
e
r
esear
ch
er
s
f
o
r
f
u
r
th
er
d
ev
elo
p
m
e
n
t
an
d
p
o
te
n
tial
ap
p
licatio
n
s
.
T
h
e
co
n
d
u
ct
o
f
t
h
e
s
tu
d
y
co
n
tr
i
b
u
tes
to
th
e
g
r
o
win
g
liter
atu
r
e
o
n
ac
ad
em
ic
p
er
f
o
r
m
an
ce
p
r
ed
ictio
n
th
r
o
u
g
h
E
DM
.
2.
M
E
T
H
O
D
Usi
n
g
d
ata
-
wr
an
g
led
Mo
o
d
le
lo
g
s
,
th
e
s
tu
d
y
u
s
ed
s
u
p
er
v
is
ed
b
in
ar
y
class
if
icatio
n
m
ac
h
in
e
lear
n
in
g
to
p
r
e
d
ict
s
tu
d
en
t
p
er
f
o
r
m
an
ce
.
Data
co
llected
f
r
o
m
a
Ph
ilip
p
in
e
u
n
i
v
er
s
ity
’
s
Mo
o
d
le
lo
g
s
wer
e
u
s
ed
to
f
ea
tu
r
e
-
en
g
in
ee
r
Mo
o
d
le
-
g
e
n
er
ic
p
r
ed
icto
r
s
o
f
s
tu
d
en
t
p
e
r
f
o
r
m
a
n
ce
.
T
h
e
f
ea
tu
r
e
en
g
i
n
ee
r
in
g
f
o
cu
s
ed
o
n
cr
ea
tin
g
th
e
o
r
etica
lly
alig
n
ed
f
ea
tu
r
es,
en
s
u
r
in
g
ap
p
licab
i
lity
to
o
t
h
er
M
o
o
d
le
co
u
r
s
es
wh
ile
aim
in
g
at
g
en
er
aliza
b
ilit
y
.
T
h
e
Mo
o
d
le
d
ata
wer
e
u
s
ed
t
o
tr
ain
m
u
ltip
le
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
an
d
c
o
m
p
a
r
e
th
eir
p
er
f
o
r
m
an
ce
.
T
h
e
b
est
-
p
e
r
f
o
r
m
in
g
alg
o
r
ith
m
was
ch
o
s
en
f
o
r
th
e
f
i
n
al
m
o
d
el.
T
h
e
f
in
al
m
o
d
el
was
test
ed
u
s
in
g
a
test
in
g
s
et
f
r
o
m
th
e
d
ata
co
llected
to
d
eter
m
i
n
e
its
p
er
f
o
r
m
an
ce
o
n
u
n
s
e
en
d
ata.
T
h
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
asp
ec
t
o
f
th
e
s
tu
d
y
was
p
er
f
o
r
m
ed
u
s
in
g
th
e
R
p
r
o
g
r
am
m
in
g
lan
g
u
ag
e.
T
h
e
m
ac
h
in
e
lear
n
in
g
asp
ec
t
was
p
er
f
o
r
m
e
d
u
s
in
g
t
h
e
s
am
e
lan
g
u
ag
e
th
r
o
u
g
h
th
e
tid
y
m
o
d
el’
s
p
ac
k
a
g
e.
T
h
e
f
o
llo
win
g
s
u
b
s
ec
tio
n
s
d
etail
th
e
s
tep
s
an
d
tech
n
iq
u
es u
s
ed
in
th
e
s
tu
d
y
.
2
.
1
.
M
o
o
dle da
t
a
s
et
a
nd
da
t
a
prepro
ce
s
s
ing
T
h
e
s
tu
d
y
’
s
d
ata
co
llectio
n
a
n
d
p
r
ep
r
o
ce
s
s
in
g
asp
ec
t
was
b
ased
o
n
th
e
s
tu
d
y
o
f
[
2
9
]
as
d
etailed
in
th
e
d
ata
co
llectio
n
an
d
f
ea
tu
r
e
en
g
in
ee
r
i
n
g
s
u
b
s
ec
tio
n
s
o
f
t
h
e
Me
th
o
d
s
ec
tio
n
.
T
h
e
d
ata
was
co
llected
f
r
o
m
th
e
Mo
o
d
le
lo
g
s
o
f
2
2
d
i
f
f
er
en
t
co
u
r
s
es
co
m
in
g
f
r
o
m
th
e
B
ac
h
elo
r
o
f
Scien
ce
in
I
n
f
o
r
m
atio
n
T
ec
h
n
o
l
o
g
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:
2
5
0
2
-
4
7
52
P
r
ed
ictin
g
s
tu
d
en
t p
erfo
r
ma
n
ce
u
s
in
g
Mo
o
d
le
d
a
ta
a
n
d
m
a
ch
in
e
lea
r
n
in
g
w
ith
…
(
J
a
ma
l
K
a
y
R
o
g
ers
)
225
(
B
SIT
)
an
d
th
e
B
ac
h
elo
r
o
f
Scien
ce
in
C
o
m
p
u
ter
Scien
ce
(
B
SC
S)
p
r
o
g
r
am
s
o
f
th
e
Un
iv
er
s
ity
o
f
So
u
th
ea
s
ter
n
Ph
ilip
p
in
es
(
USeP)
,
a
s
tate
u
n
iv
er
s
ity
in
th
e
Da
v
ao
r
eg
io
n
o
f
th
e
Ph
ilip
p
in
es.
T
h
ese
co
u
r
s
es
wer
e
f
r
o
m
3
acad
em
ic
y
ea
r
s
:
2
0
2
1
-
2
0
2
2
,
2
0
2
2
-
2
0
2
3
,
an
d
2
0
2
3
-
2
0
2
4
.
Mu
ltip
le
co
u
r
s
es
with
d
if
f
er
e
n
t
in
s
tr
u
cto
r
s
wer
e
u
s
ed
to
in
cr
ea
s
e
th
e
r
ep
r
esen
tatio
n
o
f
d
if
f
er
en
t
ty
p
es
o
f
co
u
r
s
es.
T
h
e
d
ata
s
et
af
ter
d
ata
wr
a
n
g
lin
g
is
s
tr
u
ctu
r
ed
d
ata
co
n
tain
in
g
r
o
ws
an
d
co
lu
m
n
s
.
T
h
e
r
o
ws
r
ep
r
esen
t
th
e
co
u
n
t
o
f
s
tu
d
e
n
t
’
s
lo
g
g
ed
ac
tio
n
s
b
ase
d
o
n
co
lu
m
n
v
a
r
iab
les o
r
f
ea
t
u
r
e
s
.
T
h
ese
f
ea
tu
r
es a
r
e
th
e
M
o
o
d
le
ev
en
t n
am
es f
r
o
m
th
e
M
o
o
d
le
lo
g
s
.
Featu
r
e
en
g
in
ee
r
in
g
was
b
ased
o
n
ex
is
tin
g
th
eo
r
ies.
T
h
e
m
o
s
t
u
s
ed
Mo
o
d
le
ac
tiv
ities
an
d
r
eso
u
r
ce
s
(
s
u
b
m
is
s
io
n
s
,
q
u
izze
s
,
an
d
f
o
r
u
m
s
)
wer
e
co
n
s
id
er
e
d
to
en
s
u
r
e
ap
p
licab
ilit
y
to
o
th
er
Mo
o
d
le
co
u
r
s
es.
T
h
ese
r
ep
r
esen
t
n
ec
ess
ar
y
to
o
ls
f
o
r
co
m
m
u
n
icatio
n
,
c
o
llab
o
r
atio
n
,
an
d
ass
ess
m
en
t
[
3
0
]
.
T
h
e
c
r
ea
te,
r
ea
d
,
u
p
d
ate,
an
d
d
elete
a
ctio
n
s
(
C
R
UD)
,
wh
ich
r
ep
r
esen
t
a
s
tan
d
ar
d
l
o
g
r
ec
o
r
d
(
d
atab
ase
q
u
er
y
ty
p
es)
in
in
f
o
r
m
atio
n
s
y
s
tem
s
,
wer
e
also
co
n
s
id
er
e
d
.
T
h
ese
f
ea
tu
r
es
wer
e
en
g
i
n
ee
r
ed
b
y
a
d
d
in
g
th
e
v
alu
es
o
f
th
e
ap
p
r
o
p
r
iate
Mo
o
d
le
ev
en
t
n
a
m
es
lo
g
g
ed
b
y
s
tu
d
en
ts
.
T
h
e
v
ar
ia
b
le
d
ay
s
lo
g
g
ed
in
r
ep
r
esen
ts
a
tim
e
-
b
ased
f
ea
tu
r
e
th
at
co
u
n
ts
th
e
n
u
m
b
er
o
f
d
ay
s
s
tu
d
en
ts
h
av
e
lo
g
g
e
d
in
to
th
eir
c
o
u
r
s
e.
T
h
e
eig
h
t
(
8
)
v
a
r
iab
les
ar
e
th
e
g
en
er
alize
d
f
ea
tu
r
es
o
f
th
e
s
tu
d
y
,
en
s
u
r
i
n
g
ap
p
licatio
n
s
ac
r
o
s
s
m
u
ltip
le
Mo
o
d
le
p
latf
o
r
m
s
.
T
h
es
e
f
ea
tu
r
es
ar
e
th
e
p
r
ed
icto
r
s
o
r
in
d
ep
e
n
d
en
t
v
ar
i
ab
les
o
f
th
e
s
tu
d
y
.
T
h
e
s
tu
d
y
en
g
in
ee
r
ed
an
o
th
er
f
ea
tu
r
e
,
Pe
r
f
o
r
m
a
n
ce
,
t
o
lab
el
s
tu
d
en
ts
’
p
er
f
o
r
m
an
ce
as
h
ig
h
o
r
lo
w
b
ased
o
n
c
o
u
r
s
e
g
r
ad
es.
T
h
is
f
ea
tu
r
e
is
th
e
r
esp
o
n
s
e
o
r
d
e
p
en
d
e
n
t
v
ar
iab
le.
A
co
u
r
s
e
g
r
a
d
e
o
f
8
6
an
d
ab
o
v
e
d
en
o
tes
h
ig
h
p
er
f
o
r
m
an
ce
;
else,
lo
w
p
er
f
o
r
m
a
n
ce
.
T
ab
le
1
s
h
o
ws
th
e
d
ata
s
et
s
u
m
m
ar
y
u
s
ed
in
t
h
e
s
tu
d
y
af
ter
d
ata
p
r
ep
r
o
ce
s
s
in
g
.
Fig
u
r
e
1
s
h
o
ws
th
e
d
ata
d
is
tr
ib
u
tio
n
b
ased
o
n
p
er
f
o
r
m
a
n
c
e,
wh
er
e
s
tu
d
en
ts
lab
eled
h
ig
h
ar
e
6
1
5
wh
ile
th
o
s
e
lab
eled
lo
w
ar
e
3
9
2
.
T
h
is
d
ata
is
co
n
s
id
er
ed
i
m
b
alan
ce
d
d
ata.
Fig
u
r
e
2
s
h
o
ws
th
e
co
r
r
elatio
n
s
b
etwe
en
th
e
n
u
m
er
ic
f
ea
tu
r
es
o
f
th
e
d
ata,
s
o
m
e
i
n
d
ica
tin
g
h
ig
h
co
r
r
elatio
n
s
.
T
h
e
h
ig
h
est
Pear
s
o
n
R
co
r
r
elatio
n
co
e
f
f
icien
t
is
0
.
9
5
b
etwe
en
th
e
s
u
b
m
is
s
io
n
ac
tio
n
s
an
d
cr
ea
te
ac
tio
n
s
.
Hig
h
co
r
r
elatio
n
co
e
f
f
icien
ts
o
f
0
.
7
6
an
d
0
.
7
4
ex
is
t
b
etwe
en
u
p
d
ate
ac
tio
n
s
an
d
cr
ea
te
ac
tio
n
s
,
an
d
r
ea
d
ac
tio
n
s
an
d
f
o
r
u
m
d
ay
s
lo
g
g
ed
in
,
r
esp
ec
tiv
ely
.
An
o
th
er
h
ig
h
co
r
r
elatio
n
co
ef
f
icien
t
o
f
0
.
6
9
ex
is
ts
b
etwe
en
r
ea
d
ac
tio
n
s
an
d
q
u
iz
ac
tio
n
s
.
Hig
h
co
r
r
elatio
n
s
in
th
e
d
ata
s
e
t
r
en
d
er
s
o
m
e
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
p
er
f
o
r
m
p
o
o
r
ly
,
esp
ec
ially
p
ar
am
etr
ic
alg
o
r
ith
m
s
f
o
r
class
if
icatio
n
m
o
d
elin
g
[
3
1
]
.
T
ab
le
1
.
T
h
e
s
u
m
m
ar
y
o
f
t
h
e
d
ata
s
et
u
s
ed
in
th
e
s
tu
d
y
V
a
r
i
a
b
l
e
(
f
e
a
t
u
r
e
)
n
a
me
V
a
r
i
a
b
l
e
c
l
a
ss
V
a
r
i
a
b
l
e
t
y
p
e
S
u
b
m
i
ssi
o
n
a
c
t
i
o
n
s
Q
u
i
z
a
c
t
i
o
n
s
F
o
r
u
m
a
c
t
i
o
n
s
C
r
e
a
t
e
a
c
t
i
o
n
s
R
e
a
d
a
c
t
i
o
n
s
U
p
d
a
t
e
a
c
t
i
o
n
s
D
e
l
e
t
e
a
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t
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n
s
D
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y
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e
d
i
n
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d
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c
(
d
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d
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)
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(
d
o
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b
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c
(
d
o
u
b
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e
)
N
u
meri
c
(
d
o
u
b
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N
u
meri
c
(
d
o
u
b
l
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)
N
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meri
c
(
d
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u
b
l
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)
P
r
e
d
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i
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p
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d
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P
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i
c
t
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r
(
i
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d
e
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t
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P
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d
i
c
t
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r
(
i
n
d
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p
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d
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t
)
P
r
e
d
i
c
t
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r
(
i
n
d
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p
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n
d
e
n
t
)
P
r
e
d
i
c
t
o
r
(
i
n
d
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p
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n
d
e
n
t
)
P
r
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d
i
c
t
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r
(
i
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d
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p
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n
d
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t
)
P
r
e
d
i
c
t
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r
(
i
n
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p
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n
d
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n
t
)
P
r
e
d
i
c
t
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r
(
i
n
d
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p
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n
d
e
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t
)
P
e
r
f
o
r
ma
n
c
e
C
h
a
r
a
c
t
e
r
(
f
a
c
t
o
r
l
e
v
e
l
s
:
h
i
g
h
,
l
o
w
)
R
e
s
p
o
n
se
(
d
e
p
e
n
d
e
n
t
)
N
o
.
o
f
r
o
w
s
(
o
b
ser
v
a
t
i
o
n
s)
N
o
.
o
f
c
o
l
u
m
n
s (f
e
a
t
u
r
e
s)
:
1
0
0
7
:
9
Fig
u
r
e
1
.
Per
f
o
r
m
an
c
e
d
is
tr
ib
u
tio
n
o
f
th
e
Mo
o
d
le
d
ata
b
ased
o
n
p
er
f
o
r
m
a
n
ce
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
223
-
2
3
1
226
Fig
u
r
e
2
.
C
o
r
r
elatio
n
p
lo
t
o
f
t
h
e
f
ea
tu
r
es o
f
th
e
d
ata
s
et
s
h
o
win
g
P
ea
r
s
o
n
r
c
o
ef
f
icien
ts
2
.
2
.
M
a
chine le
a
rning
Du
e
to
th
eir
n
o
n
-
p
ar
a
m
etr
ic
n
atu
r
e,
m
u
ltip
le
tr
ee
-
b
ased
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
w
er
e
u
s
ed
to
tr
ain
f
o
r
p
r
ed
ictiv
e
a
n
aly
tics
.
T
h
e
m
o
d
elin
g
f
r
am
ewo
r
k
in
F
ig
u
r
e
3
was
u
s
ed
as
th
e
g
u
id
in
g
f
r
a
m
ewo
r
k
o
f
th
e
s
tu
d
y
.
T
h
e
d
ata
s
et
was
s
p
lit
i
n
to
tr
ain
in
g
a
n
d
test
in
g
s
ets.
R
esam
p
lin
g
was
p
er
f
o
r
m
e
d
o
n
t
h
e
tr
ain
in
g
s
et,
an
d
tr
ee
-
b
ased
m
o
d
els
wer
e
co
m
p
ar
ed
.
T
h
e
b
est
-
p
e
r
f
o
r
m
in
g
m
o
d
el
was
s
elec
ted
an
d
u
s
ed
to
f
it
th
e
en
tire
tr
ain
in
g
d
ata.
T
h
e
b
est m
o
d
el
’
s
p
er
f
o
r
m
an
ce
o
n
tr
ain
in
g
was v
e
r
if
ie
d
o
n
th
e
test
in
g
s
et.
Fig
u
r
e
3
.
T
h
e
m
o
d
elin
g
f
r
am
e
wo
r
k
u
s
ed
in
th
e
s
tu
d
y
3
m
etr
ics
wer
e
u
s
ed
to
m
ea
s
u
r
e
m
o
d
el
p
er
f
o
r
m
an
ce
:
R
OC
-
AUC
(
r
ec
eiv
er
o
p
e
r
atin
g
c
h
ar
ac
ter
is
tics
–
ar
ea
u
n
d
er
th
e
c
u
r
v
e
)
,
s
en
s
itiv
ity
,
an
d
s
p
ec
if
icity
.
T
h
e
R
OC
-
AUC
i
s
a
g
r
ap
h
ical
r
e
p
r
esen
tatio
n
o
f
th
e
tr
ad
e
-
o
f
f
b
etwe
en
th
e
tr
u
e
p
o
s
itiv
e
r
ate
(
T
PR
o
r
s
en
s
it
iv
ity
)
o
n
th
e
x
-
ax
is
an
d
th
e
f
alse
p
o
s
itiv
e
r
ate
(
FP
R
o
r
1
-
sp
ec
if
icity
)
o
n
th
e
y
-
a
x
is
at
v
ar
io
u
s
s
ettin
g
s
o
f
th
e
m
o
d
e
l.
I
t
q
u
an
tifie
s
a
m
o
d
el
’
s
ab
i
lity
to
d
is
tin
g
u
is
h
b
etwe
en
p
o
s
itiv
e
a
n
d
n
eg
ativ
e
ca
s
es
ac
r
o
s
s
all
p
o
s
s
ib
le
th
r
esh
o
ld
s
.
A
p
er
f
ec
t
m
o
d
el
will
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Sen
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t
r
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n
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ativ
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ate
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T
NR
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m
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c
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y
th
e
m
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d
el.
R
OC
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AU
C
=
∫
(
)
1
0
(
1
)
T
PR
=
+
(
2
)
T
NR
=
+
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
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4
7
52
P
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tu
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t p
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J
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227
I
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tu
d
y
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t
h
e
h
ig
h
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p
er
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o
r
m
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ce
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ile
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.
1
.
Da
t
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s
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t
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ing
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s
a
m
pli
ng
T
h
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d
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m
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t
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b
l
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s
,
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d
u
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g
b
i
a
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s
[
3
2
]
.
T
h
e
t
r
a
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t
c
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4
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a
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d
3
1
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o
w
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p
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n
s
1
2
3
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n
d
7
9
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e
s
p
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t
i
v
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y
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o
l
l
o
w
i
n
g
t
h
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f
r
a
m
e
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k
i
n
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i
g
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r
e
2
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t
h
e
t
r
a
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o
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,
w
h
i
l
e
t
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r
e
m
a
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n
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n
g
9
a
r
e
t
r
a
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n
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n
g
s
e
t
s
.
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h
i
s
t
y
p
e
o
f
v
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d
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t
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s
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l
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k
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g
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d
u
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g
t
h
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t
r
a
i
n
i
n
g
[
3
3
]
.
R
e
s
a
m
p
l
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n
g
p
r
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v
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d
e
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m
o
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n
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s
t
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p
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.
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a
b
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2
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o
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m
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1
0
-
f
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o
s
s
-
v
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l
i
d
a
t
i
o
n
.
T
ab
le
2
.
R
esam
p
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r
o
m
t
h
e
t
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ain
in
g
s
et
u
s
in
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alid
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Tr
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t
Te
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d
0
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2
3
82
F
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3
82
F
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l
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2
4
81
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80
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d
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0
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5
80
2.
2
.
2
.
M
o
delin
g
a
nd
v
a
ria
ble im
po
rt
a
nce
T
h
e
s
tu
d
y
co
m
p
a
r
ed
3
tr
ee
-
b
a
s
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
f
o
r
tr
ain
in
g
:
XGBo
o
s
t,
RF
,
an
d
C
5
.
0
DT
.
T
h
ese
alg
o
r
ith
m
s
wer
e
ch
o
s
en
b
ec
a
u
s
e
o
f
th
eir
e
x
p
l
ain
ab
ilit
y
an
d
in
ter
p
r
etab
ilit
y
[
3
4
]
an
d
ab
ilit
y
t
o
p
er
f
o
r
m
well
with
lim
ited
d
ata
[
3
5
]
,
m
ak
in
g
th
em
s
u
ita
b
le
f
o
r
d
ea
lin
g
with
h
ig
h
l
y
co
r
r
el
ated
f
ea
tu
r
es.
I
n
t
h
e
co
m
p
ar
is
o
n
,
th
e
h
y
p
er
p
ar
am
et
er
s
o
f
th
e
alg
o
r
ith
m
s
wer
e
tu
n
ed
to
g
et
th
e
b
est
p
ar
am
eter
co
m
b
in
atio
n
s
f
r
o
m
ea
ch
.
Hy
p
er
p
a
r
am
eter
tu
n
in
g
g
en
er
ated
m
u
ltip
le
m
o
d
els
b
ased
o
n
ea
ch
co
m
b
in
atio
n
.
T
h
e
tu
n
e
r
ac
e
ANOV
A
m
eth
o
d
f
r
o
m
tid
y
m
o
d
els
was
u
s
ed
d
u
r
in
g
tr
ain
in
g
to
s
h
o
r
t
en
tr
ain
in
g
tim
e
b
y
elim
in
ati
n
g
p
o
o
r
-
p
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f
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r
m
in
g
m
o
d
els.
A
r
ec
i
p
e
p
r
ep
r
o
ce
s
s
o
r
f
r
o
m
t
h
e
tid
y
m
o
d
els
p
ac
k
ag
e
was
s
et
to
n
o
r
m
alize
t
h
e
tr
ain
in
g
d
ata
a
n
d
o
v
er
s
am
p
le
th
e
m
i
n
o
r
ity
class
(
lo
w)
u
s
in
g
th
e
a
d
ap
tiv
e
s
y
n
t
h
etic
(
ADASYN)
s
am
p
lin
g
ap
p
r
o
ac
h
.
ADASYN
was u
s
ed
to
im
p
r
o
v
e
th
e
alg
o
r
ith
m
s
’
p
er
f
o
r
m
an
ce
o
n
th
e
im
b
alan
ce
d
d
ata
s
et
d
u
r
in
g
tr
ain
i
n
g
[
3
6
]
,
[
3
7
]
.
T
h
e
im
p
o
r
tan
ce
o
f
th
e
f
ea
tu
r
e
s
o
f
th
e
m
o
d
elin
g
p
e
r
f
o
r
m
ed
was
d
eter
m
in
ed
with
th
e
h
elp
o
f
th
e
v
i
p
p
ac
k
ag
e
in
R
.
T
h
e
v
ip
p
ac
k
ag
e
v
is
u
alize
s
th
e
s
tr
en
g
th
o
f
th
e
r
elatio
n
s
h
ip
b
etwe
en
e
a
ch
f
ea
tu
r
e
an
d
th
e
r
esp
o
n
s
e
p
r
ed
icted
w
h
ile
co
n
s
id
er
in
g
all
o
th
er
f
ea
t
u
r
es
u
s
ed
in
m
o
d
elin
g
.
T
h
e
p
ac
k
a
g
e
ca
lcu
lates
v
ar
iab
le
im
p
o
r
tan
ce
(
V
I
)
s
co
r
es
u
s
i
n
g
m
o
d
el
-
s
p
ec
if
ic
o
r
m
o
d
e
l
-
ag
n
o
s
tic
ap
p
r
o
ac
h
es
s
u
ch
as
v
ar
ian
ce
-
b
ased
,
p
er
m
u
tatio
n
,
an
d
Sh
a
p
ley
m
et
h
o
d
s
[
3
8
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Co
m
pa
risi
o
n a
nd
s
elec
t
io
n o
f
bes
t
m
o
del
T
h
e
s
tu
d
y
co
m
p
ar
ed
an
d
r
an
k
ed
th
e
t
u
n
ed
alg
o
r
ith
m
s
’
p
er
f
o
r
m
an
ce
o
n
tr
ain
in
g
b
ased
o
n
3
m
etr
ics
:
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
R
OC
-
AU
C
.
T
h
is
ap
p
r
o
ac
h
i
s
also
ev
id
en
t
in
th
e
s
tu
d
ies
o
f
[
1
7
]
,
[
2
0
]
-
[
2
3
]
,
wh
er
ein
th
ey
co
m
p
ar
ed
m
u
l
tip
le
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
ac
r
o
s
s
m
u
ltip
le
m
etr
i
cs.
T
h
e
m
o
d
elin
g
co
n
d
u
cte
d
in
th
e
s
tu
d
y
g
e
n
er
a
ted
m
u
ltip
le
m
o
d
els,
an
d
g
o
o
d
-
p
er
f
o
r
m
in
g
m
o
d
els
wer
e
r
a
n
k
ed
as
d
eter
m
in
e
d
b
y
th
e
tu
n
e
r
a
ce
ANOV
A
m
eth
o
d
.
B
ased
o
n
th
e
R
OC
-
AUC
s
co
r
e,
th
e
RF
m
o
d
el
p
er
f
o
r
m
ed
b
est
at
0
.
7
7
with
a
s
en
s
itiv
ity
o
f
0
.
7
3
a
n
d
s
p
ec
i
f
icity
o
f
0
.
6
6
.
T
h
e
C
5
.
0
DT
c
o
m
es
in
s
ec
o
n
d
p
lace
with
a
n
R
OC
-
AU
C
s
co
r
e
o
f
0
.
7
4
an
d
s
en
s
itiv
ity
an
d
s
p
ec
if
icity
o
f
0
.
6
7
an
d
0
.
7
2
,
r
esp
ec
ti
v
ely
.
L
ast
p
lace
is
th
e
XGBo
o
s
t
with
R
OC
-
A
C
U
o
f
0
.
7
1
,
s
en
s
itiv
ity
o
f
0
.
7
0
,
an
d
s
p
ec
if
icity
o
f
0
.
5
7
.
T
a
b
le
3
s
u
m
m
ar
izes
th
e
r
esu
lts
o
f
th
es
e
m
o
d
els
b
ased
o
n
th
e
3
m
etr
ics
with
p
r
ef
er
en
c
e
to
th
e
R
OC
-
AUC
m
etr
ic.
T
h
e
R
OC
-
AUC
wa
s
ch
o
s
en
as
th
e
ap
p
r
o
p
r
iate
r
ef
er
en
ce
m
etr
ic
b
ec
au
s
e
o
f
th
e
im
b
alan
ce
d
n
atu
r
e
o
f
th
e
d
ata,
an
d
it
is
a
p
o
p
u
lar
m
etr
ic
f
o
r
b
in
ar
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
223
-
2
3
1
228
class
if
icatio
n
[
3
9
]
.
T
h
e
RF
m
o
d
el
was
ch
o
s
en
as
th
e
o
v
e
r
all
b
est
m
o
d
el,
co
m
p
lem
en
tin
g
th
e
s
tu
d
ies
o
f
[
2
0
]
,
[
2
2
]
,
[
2
3
]
,
b
u
t
with
d
if
f
e
r
en
t
s
ets
o
f
m
etr
ics.
T
h
e
RF
m
o
d
el
h
as
th
e
h
ig
h
est
v
alu
e
in
d
is
tin
g
u
is
h
in
g
h
ig
h
-
an
d
lo
w
-
p
er
f
o
r
m
in
g
s
tu
d
en
ts
.
T
h
e
m
o
d
el
is
also
b
est
at
p
r
e
d
ictin
g
h
ig
h
-
pe
r
f
o
r
m
in
g
s
tu
d
en
ts
w
h
ile
in
s
ec
o
n
d
p
lace
at
p
r
ed
ictin
g
lo
w
-
p
er
f
o
r
m
in
g
s
tu
d
en
ts
.
T
ab
le
3
.
Su
m
m
a
r
y
r
esu
lts
o
f
th
e
b
est tr
ee
-
b
ased
m
o
d
els ac
r
o
s
s
3
m
etr
ics
M
o
d
e
l
R
O
C
-
AUC
S
e
n
s
i
t
i
v
i
t
y
S
p
e
c
i
f
i
c
i
t
y
RF
0
.
7
7
0
.
7
3
0
.
6
6
C
5
.
0
DT
0
.
7
4
0
.
6
7
0
.
7
2
X
G
B
o
o
st
0
.
7
1
0
.
7
0
0
.
5
7
3.
2
.
F
ina
l
m
o
del f
it
a
nd
v
er
if
ica
t
io
n o
f
m
o
del per
f
o
r
m
a
n
ce
T
h
e
ch
o
s
en
b
est
m
o
d
el
was
f
itted
o
n
th
e
test
in
g
s
et,
u
n
s
ee
n
d
ata
d
u
r
in
g
d
ata
tr
ai
n
in
g
,
to
v
alid
ate
th
e
m
o
d
el
’
s
o
v
er
all
p
er
f
o
r
m
an
ce
.
T
h
e
ch
o
s
en
m
o
d
el
’
s
p
er
f
o
r
m
a
n
ce
o
n
th
e
test
in
g
s
et
ac
r
o
s
s
all
m
etr
ics
is
s
h
o
wn
in
T
ab
le
4
.
I
ts
R
OC
-
AUC
s
co
r
e
o
n
th
e
test
in
g
s
et
is
0
.
7
3
.
T
h
e
m
o
d
el
class
if
ied
h
ig
h
-
p
er
f
o
r
m
in
g
s
tu
d
e
n
ts
with
a
s
en
s
itiv
ity
s
co
r
e
o
f
0
.
6
9
.
T
h
e
m
o
d
el
also
class
if
ied
lo
w
-
p
e
r
f
o
r
m
in
g
s
tu
d
en
ts
with
a
s
p
ec
i
f
icity
s
co
r
e
o
f
0
.
6
6
.
Fig
u
r
e
4
s
h
o
ws
th
e
p
lo
t
o
f
th
e
R
OC
-
AUC.
T
h
e
p
lo
t
in
d
icate
s
th
at
th
e
m
o
d
el
h
as
a
m
o
d
e
r
ate
d
is
cr
im
in
ato
r
y
ab
ilit
y
,
b
etter
th
an
r
a
n
d
o
m
g
u
ess
in
g
b
u
t
n
o
t
h
ig
h
ly
ac
cu
r
ate.
T
h
e
m
o
d
el
ca
p
tu
r
es
a
d
ec
e
n
t
p
o
r
tio
n
o
f
h
ig
h
-
p
er
f
o
r
m
in
g
s
t
u
d
en
t
ca
s
es.
Ho
wev
er
,
th
er
e
i
s
r
o
o
m
f
o
r
im
p
r
o
v
em
en
t
in
i
n
cr
ea
s
in
g
th
e
T
PR
.
T
h
e
m
o
d
el
is
also
s
o
m
ewh
at
ef
f
ec
tiv
e
at
av
o
id
in
g
f
als
e
p
o
s
itiv
es.
Ho
wev
er
,
th
er
e
is
al
s
o
r
o
o
m
f
o
r
im
p
r
o
v
em
e
n
t
in
in
cr
ea
s
in
g
th
e
T
NR
.
I
n
cr
ea
s
in
g
th
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
m
a
y
b
e
ac
h
ie
v
ed
wi
th
in
cr
ea
s
ed
d
ata
s
ize
[
4
0
]
.
T
ab
le
4
.
T
h
e
ch
o
s
en
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
o
n
th
e
test
in
g
s
et
M
o
d
e
l
R
O
C
-
AUC
S
e
n
s
i
t
i
v
i
t
y
S
p
e
c
i
f
i
c
i
t
y
RF
0
.
7
3
0
.
6
9
0
.
6
6
Fig
u
r
e
4
.
Plo
t o
f
th
e
R
OC
-
AUC p
er
f
o
r
m
a
n
ce
o
f
t
h
e
m
o
d
el
a
cr
o
s
s
d
if
f
er
en
t th
r
esh
o
ld
s
ettin
g
s
3.
3
.
F
e
a
t
ure
im
po
rt
a
nce
inte
rpre
t
a
t
io
n
T
h
e
s
tu
d
y
o
f
[
4
1
]
s
h
o
wed
d
if
f
er
en
t
p
atter
n
s
o
f
h
o
w
s
tu
d
en
ts
in
ter
ac
t
with
Mo
o
d
le
b
ased
o
n
co
n
ten
t,
ac
tiv
ities
,
an
d
ass
e
s
s
m
en
t.
T
h
er
ef
o
r
e,
s
elec
tin
g
th
e
m
o
s
t im
p
o
r
tan
t f
ea
tu
r
es in
m
o
d
eli
n
g
c
an
s
ig
n
if
ican
tly
h
elp
im
p
r
o
v
e
th
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
[
4
2
]
.
T
h
e
f
ea
tu
r
es
wer
e
r
an
k
ed
f
r
o
m
h
ig
h
est
to
lo
west
in
im
p
o
r
tan
ce
,
wh
er
e
th
e
m
o
s
t
cr
u
cial
f
ea
tu
r
e
is
lis
ted
f
ir
s
t
an
d
th
e
least
im
p
o
r
tan
t
is
lis
ted
last
.
T
ab
le
5
s
h
o
ws
th
e
VI
s
co
r
es
o
f
ea
ch
f
ea
tu
r
e.
T
h
e
s
u
b
m
is
s
io
n
ac
tio
n
s
h
a
v
e
b
ee
n
i
d
en
tifie
d
as
th
e
m
o
s
t
cr
u
cial
f
ea
tu
r
e,
with
a
VI
s
co
r
e
o
f
7
2
.
7
3
.
T
h
is
s
u
g
g
ests
th
at
ac
tio
n
s
r
elate
d
to
s
u
b
m
is
s
io
n
s
ar
e
th
e
s
tr
o
n
g
est
p
r
ed
icto
r
o
f
ac
a
d
em
ic
p
er
f
o
r
m
an
ce
.
T
h
is
f
in
d
in
g
c
o
m
p
lem
e
n
ts
th
e
s
tu
d
ies
o
f
[
2
1
]
,
[
2
2
]
,
r
ef
er
r
i
n
g
to
s
u
b
m
itti
n
g
ass
ig
n
m
e
n
ts
with
in
Mo
o
d
le
as
th
e
b
est o
r
o
n
e
o
f
th
e
b
est p
r
ed
ict
o
r
s
o
f
ac
ad
e
m
ic
p
er
f
o
r
m
a
n
ce
.
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:
2
5
0
2
-
4
7
52
P
r
ed
ictin
g
s
tu
d
en
t p
erfo
r
ma
n
ce
u
s
in
g
Mo
o
d
le
d
a
ta
a
n
d
m
a
ch
in
e
lea
r
n
in
g
w
ith
…
(
J
a
ma
l
K
a
y
R
o
g
ers
)
229
T
h
e
r
ea
d
ac
tio
n
s
,
d
ay
s
lo
g
g
e
d
in
,
an
d
cr
ea
te
ac
tio
n
s
ar
e
c
lo
s
e
in
s
ec
o
n
d
,
th
ir
d
,
an
d
f
o
u
r
th
p
lace
s
,
r
esp
ec
tiv
ely
.
W
ith
a
VI
s
co
r
e
o
f
5
7
.
5
3
,
r
ea
d
ac
tio
n
s
s
ig
n
if
i
es
th
at
r
ea
d
in
g
o
r
ac
ce
s
s
in
g
c
o
n
ten
t
is
cr
u
cial
to
ac
ad
em
ic
p
er
f
o
r
m
an
ce
.
T
h
e
d
ay
s
lo
g
g
ed
in
,
h
a
v
in
g
a
VI
s
co
r
e
o
f
5
5
.
8
0
,
s
u
g
g
ests
th
at
co
n
s
is
ten
t
en
g
ag
em
en
t
with
th
e
Mo
o
d
le
L
MS
is
n
o
t
ab
le
in
p
r
e
d
ictin
g
ac
ad
e
m
ic
p
er
f
o
r
m
an
ce
,
c
o
n
s
is
ten
t
with
th
e
s
tu
d
ies
o
f
[
2
1
]
,
[
2
3
]
.
T
h
e
cr
ea
te
ac
tio
n
s
im
p
lies
th
at
ac
tiv
ities
r
elate
d
to
cr
ea
tin
g
co
n
ten
t
with
in
th
e
Mo
o
d
le
L
MS
also
s
tr
o
n
g
ly
in
f
l
u
en
ce
ac
ad
e
m
ic
p
er
f
o
r
m
a
n
ce
,
with
a
VI
s
co
r
e
o
f
5
5
.
1
0
.
Qu
iz
a
ctio
n
s
co
m
es
in
at
f
if
t
h
p
lace
with
a
VI
s
co
r
e
o
f
4
8
.
1
3
,
im
p
ly
in
g
th
at
in
ter
a
ctio
n
s
with
in
q
u
izze
s
o
r
ass
ess
m
en
ts
with
in
Mo
o
d
le
ca
n
s
till
in
f
lu
en
ce
ac
ad
em
ic
p
er
f
o
r
m
an
ce
.
T
h
e
f
o
r
u
m
ac
tio
n
s
,
in
ter
ac
tio
n
s
with
in
f
o
r
u
m
s
an
d
d
is
cu
s
s
io
n
s
ca
n
also
in
f
lu
en
ce
ac
ad
em
ic
p
er
f
o
r
m
a
n
ce
with
a
VI
s
co
r
e
o
f
4
5
.
5
an
d
r
an
k
ed
s
ix
th
.
R
an
k
ed
in
s
ev
en
th
p
lace
is
th
e
u
p
d
ate
a
ctio
n
s
,
in
d
icatin
g
th
at
s
tu
d
e
n
t
u
p
d
ates
with
in
th
e
L
MS
h
av
e
a
r
elativ
ely
l
o
wer
im
p
ac
t
o
n
p
r
e
d
ictin
g
ac
a
d
em
i
c
p
er
f
o
r
m
an
ce
,
with
a
VI
s
co
r
e
o
f
3
7
.
3
9
.
W
h
ile
s
till
r
elev
an
t
,
u
p
d
ate
ac
tio
n
s
’
im
p
ac
t
o
n
ac
ad
em
ic
p
er
f
o
r
m
an
ce
ap
p
ea
r
s
to
b
e
less
s
ig
n
if
ican
t
co
m
p
ar
e
d
to
th
e
f
ea
tu
r
es
th
at
a
r
e
h
i
g
h
er
r
an
k
e
d
.
Dele
te
a
ctio
n
s
ar
e
th
e
least
im
p
o
r
tan
t,
with
a
VI
s
co
r
e
o
f
4
,
2
2
.
T
h
is
s
u
g
g
ests
th
at
r
em
o
v
in
g
co
n
ten
t f
r
o
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ased
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icate
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ate
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m
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r
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s
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ab
ly
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r
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r
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m
th
e
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ata
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llected
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r
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m
USeP
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s
u
b
m
is
s
io
n
ac
tio
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s
,
q
u
iz
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tio
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s
,
f
o
r
u
m
ac
tio
n
s
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c
r
ea
te
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tio
n
s
,
r
ea
d
ac
tio
n
s
,
u
p
d
ate
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tio
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s
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d
elete
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tio
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s
,
d
ay
s
lo
g
g
e
d
in
,
an
d
p
er
f
o
r
m
a
n
ce
as
th
e
lab
el
f
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t
u
r
e
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r
esp
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n
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e)
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f
ea
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ar
e
aim
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en
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d
ca
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e.
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m
is
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io
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ar
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th
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m
o
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ile
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li
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tin
g
th
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im
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th
e
s
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d
y
laid
th
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g
r
o
u
n
d
wo
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k
f
o
r
f
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tu
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in
v
esti
g
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to
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o
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els
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in
f
o
r
m
ta
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s
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r
e
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t
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al
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n
ee
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s
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t
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r
ee
p
r
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SC
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d
2
2
co
u
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es,
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esu
ltin
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in
a
r
elativ
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m
all
s
am
p
le
s
ize
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f
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0
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7
o
b
s
er
v
at
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n
s
.
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eless
,
th
e
s
tu
d
y
’
s
d
ata
s
ize
is
m
o
r
e
s
ig
n
if
ican
t
in
n
u
m
b
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c
o
m
p
a
r
ed
to
p
r
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u
s
s
tu
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ies.
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we
v
er
,
th
e
f
in
d
i
n
g
s
s
h
o
w
th
at
th
e
r
e
is
m
u
ch
r
o
o
m
f
o
r
im
p
r
o
v
em
e
n
t.
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tu
r
e
r
esear
ch
co
u
ld
b
r
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ad
en
its
s
co
p
e
to
e
n
co
m
p
ass
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d
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n
al
p
r
o
g
r
a
m
s
an
d
o
t
h
er
f
ea
t
u
r
es
f
o
r
im
p
r
o
v
ed
g
en
er
aliza
b
ilit
y
.
Fu
tu
r
e
r
esear
ch
co
u
l
d
also
ex
ten
d
th
e
s
tu
d
y
to
o
th
er
u
n
iv
er
s
ities
an
d
co
n
s
id
er
o
th
er
L
MS
p
latf
o
r
m
s
o
u
ts
id
e
Mo
o
d
le.
F
u
tu
r
e
s
tu
d
ies
ca
n
c
o
n
s
id
er
m
o
d
el
d
e
p
lo
y
m
e
n
t,
al
lo
win
g
ed
u
ca
to
r
s
to
lev
er
ag
e
th
e
m
o
d
el
th
r
o
u
g
h
in
f
o
r
m
atio
n
s
y
s
tem
s
an
d
d
ash
b
o
ar
d
s
.
Ov
er
all,
th
e
s
tu
d
y
’
s
f
in
d
in
g
s
u
n
d
er
s
co
r
e
th
e
p
o
ten
tial
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
in
en
h
an
cin
g
ed
u
ca
tio
n
al
in
ter
v
e
n
tio
n
s
t
r
ateg
ies,
wh
ich
ca
n
p
av
e
th
e
way
f
o
r
ef
f
ec
tiv
e
s
u
p
p
o
r
t
m
ec
h
a
n
is
m
s
f
o
r
s
tu
d
en
ts
.
E
d
u
ca
to
r
s
ca
n
f
o
s
ter
a
m
o
r
e
i
n
clu
s
iv
e
lear
n
in
g
e
n
v
ir
o
n
m
en
t
b
y
im
p
lem
en
tin
g
tailo
r
ed
s
u
p
p
o
r
t
m
ec
h
an
is
m
s
.
T
h
e
s
tu
d
y
c
o
n
tr
ib
u
tes
to
th
e
g
r
o
win
g
b
o
d
y
o
f
liter
atu
r
e
o
n
th
e
in
ter
s
ec
t
io
n
o
f
d
ata
s
cien
ce
an
d
ed
u
ca
tio
n
,
o
f
f
er
in
g
p
ath
way
s
f
o
r
im
p
r
o
v
i
n
g
s
tu
d
en
t a
ca
d
em
ic
p
er
f
o
r
m
a
n
ce
in
o
n
lin
e
lear
n
in
g
en
v
i
r
o
n
m
en
ts
.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
s
th
a
n
k
th
e
Un
iv
e
r
s
ity
o
f
So
u
t
h
ea
s
ter
n
Ph
ilip
p
in
es
(
USeP)
f
o
r
allo
win
g
u
s
to
ac
ce
s
s
an
d
u
s
e
th
eir
M
o
o
d
le
d
ata,
wh
ic
h
was in
s
tr
u
m
en
tal
to
th
is
s
tu
d
y
.
T
h
e
co
llab
o
r
atio
n
with
USeP e
n
ab
led
u
s
to
d
elv
e
in
to
v
alu
ab
le
in
s
ig
h
ts
an
d
f
u
r
th
er
u
n
d
er
s
tan
d
th
e
r
esear
ch
s
u
b
ject.
T
h
e
s
u
p
p
o
r
t
f
r
o
m
USeP
s
ig
n
if
ican
tly
co
n
tr
ib
u
ted
to
th
e
s
u
cc
ess
o
f
t
h
is
en
d
ea
v
o
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
223
-
2
3
1
230
RE
F
E
R
E
NC
E
S
[
1
]
R
.
A
.
N
.
A
l
-
T
a
me
e
mi
e
t
a
l
.
,
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D
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mi
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o
f
p
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a
c
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m
i
c
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f
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man
c
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n
g
u
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e
r
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a
d
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t
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c
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i
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r
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r
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f
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d
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t
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4
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n
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2
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3
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d
o
i
:
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6
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i
j
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d
r
o
.
2
0
2
3
.
1
0
0
2
3
2
.
[
2
]
M
.
S
.
B
e
n
o
z
a
a
n
d
T.
P
a
l
a
o
a
g
,
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x
p
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c
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l
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a
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-
IT
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l
a
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s
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a
t
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n
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l
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o
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A
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v
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m
p
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t
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,
2
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3
[
O
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l
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e
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.
A
v
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b
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:
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w
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i
j
a
c
sa.
t
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sa
i
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o
r
g
[
3
]
K
.
K
a
n
a
t
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i
,
J
.
J
a
y
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sh
a
n
k
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r
,
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.
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y
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t
h
r
i
,
a
n
d
A
.
K
u
m
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r
,
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t
u
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t
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s
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r
f
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man
c
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f
f
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p
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d
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Re
se
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o
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p
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.
[
4
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A
.
B
o
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,
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a
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ma
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,
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o
f
In
fo
rm
a
ti
o
n
a
n
d
Co
m
p
u
ti
n
g
(CIC)
o
f
th
e
Un
iv
e
rsity
o
f
S
o
u
t
h
e
a
ste
rn
P
h
i
li
p
p
in
e
s
(USeP)
in
Da
v
a
o
Cit
y
,
P
h
il
i
p
p
i
n
e
s.
En
g
r.
R
o
g
e
rs
is
re
g
i
ste
re
d
e
lec
tro
n
ics
e
n
g
i
n
e
e
r
with
a
Ba
c
h
e
lo
r
’
s
in
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
E
n
g
in
e
e
rin
g
a
n
d
a
M
a
ste
r
’
s
in
En
g
in
e
e
rin
g
,
m
a
jo
rin
g
i
n
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
.
He
is
tak
i
n
g
a
Do
c
to
r
o
f
P
h
il
o
s
o
p
h
y
in
Ed
u
c
a
ti
o
n
m
a
jo
r
i
n
In
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
I
n
teg
ra
ti
o
n
.
His
re
se
a
rc
h
in
tere
sts
a
re
th
e
Io
T
,
d
a
ta
sc
ien
c
e
,
m
a
c
h
in
e
lea
rn
in
g
,
a
n
d
e
d
u
c
a
ti
o
n
a
l
d
a
t
a
m
in
in
g
(EDM
).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jam
a
lk
a
y
.
ro
g
e
rs@
u
se
p
.
e
d
u
.
p
h
.
Ta
m
a
r
a
Cher
Mer
c
a
d
o
is
a
p
ro
fe
ss
o
r
a
t
th
e
Co
ll
e
g
e
o
f
In
fo
rm
a
ti
o
n
a
n
d
Co
m
p
u
ti
n
g
o
f
th
e
Un
i
v
e
rsity
o
f
S
o
u
t
h
e
a
ste
rn
P
h
il
i
p
p
i
n
e
s
(USeP)
in
Da
v
a
o
Cit
y
,
P
h
il
i
p
p
in
e
s
.
S
h
e
is
t
h
e
Vic
e
P
re
sid
e
n
t
f
o
r
P
lan
n
i
n
g
a
n
d
Qu
a
li
t
y
As
su
ra
n
c
e
(V
P
P
Qu
A).
Dr
.
M
e
rc
a
d
o
h
o
ld
s
a
Do
c
to
r
o
f
P
h
il
o
s
o
p
h
y
i
n
In
f
o
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
,
a
M
a
ste
r
o
f
S
c
ien
c
e
in
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
,
a
n
d
a
Ba
c
h
e
lo
r
o
f
S
c
ie
n
c
e
in
Co
m
p
u
ter
En
g
in
e
e
ri
n
g
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
in
fo
rm
a
ti
o
n
s
y
ste
m
s,
k
n
o
wle
d
g
e
m
a
n
a
g
e
m
e
n
t,
a
n
d
o
ff
li
n
e
we
b
se
r
v
ice
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
tam
m
y
@u
se
p
.
e
d
u
.
p
h
.
Re
m
e
ly
n
Cheng
is
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
a
t
t
h
e
Co
ll
e
g
e
o
f
Art
s
a
n
d
S
c
ien
c
e
s
o
f
th
e
M
a
p
u
a
M
a
lay
a
n
C
o
ll
e
g
e
s
M
in
d
a
n
a
o
(M
M
CM
)
i
n
Da
v
a
o
Cit
y
,
P
h
il
ip
p
i
n
e
s.
Dr.
Ch
e
n
g
h
o
l
d
s
a
Do
c
to
r
o
f
P
h
il
o
so
p
h
y
i
n
M
a
th
e
m
a
ti
c
a
l
S
c
ien
c
e
s,
a
M
a
st
e
r
o
f
Arts
i
n
Ed
u
c
a
ti
o
n
i
n
Tea
c
h
in
g
M
a
t
h
e
m
a
ti
c
s,
a
n
d
a
Ba
c
h
e
lo
r
o
f
S
c
ien
c
e
in
M
a
th
e
m
a
t
ics
.
He
r
re
s
e
a
rc
h
in
tere
sts
in
c
lu
d
e
p
h
e
n
o
m
e
n
o
n
-
b
a
se
d
lea
rn
in
g
a
n
d
a
ll
th
i
n
g
s
m
a
th
e
m
a
ti
c
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
re
m
y
a
sa
h
id
@g
m
a
il
.
c
o
m
.
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