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terv
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with
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th
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I
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d
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sia
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c
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n
tex
t.
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ey
w
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d
s
:
A
r
tific
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n
eu
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al
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etwo
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C
R
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d
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m
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f
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m
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T
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s
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CC B
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SA
li
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se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Har
r
y
Dh
ik
a
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
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r
in
g
,
U
n
iv
er
s
itas
I
n
d
r
ap
r
asta PGR
I
So
u
th
J
ak
ar
ta
,
J
ak
ar
ta,
I
n
d
o
n
e
s
ia
E
m
ail:
d
h
ik
a@
u
n
in
d
r
a.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Ma
th
em
atics
lear
n
in
g
d
if
f
ic
u
lties
ar
e
a
m
aj
o
r
is
s
u
e
f
r
eq
u
en
tly
ex
p
er
ien
ce
d
b
y
I
n
d
o
n
e
s
ian
h
ig
h
s
ch
o
o
l
s
tu
d
en
ts
.
As
a
c
o
r
e
s
u
b
ject
ess
en
tial
f
o
r
d
ev
elo
p
in
g
lo
g
ical
a
n
d
a
n
aly
tical
r
ea
s
o
n
in
g
,
m
ath
em
atics
o
f
ten
co
n
tr
i
b
u
tes
s
ig
n
if
ican
tly
to
s
tu
d
en
ts
’
lo
w
ac
ad
em
ic
ac
h
iev
em
e
n
t
[
1
]
,
[
2
]
.
T
h
ese
d
i
f
f
icu
lties
ar
is
e
n
o
t
o
n
ly
f
r
o
m
co
g
n
itiv
e
lim
itatio
n
s
b
u
t
ar
e
also
clo
s
ely
ass
o
ciate
d
with
af
f
ec
tiv
e
f
ac
t
o
r
s
s
u
ch
as
an
x
iety
,
lo
w
m
o
tiv
atio
n
,
a
n
d
lack
o
f
in
ter
est in
lear
n
in
g
m
at
h
em
atics
[
3
]
–
[
5
]
.
I
n
m
o
s
t
s
ch
o
o
ls
,
th
e
id
en
tific
atio
n
o
f
lear
n
in
g
d
if
f
icu
ltie
s
s
ti
ll
r
elies
o
n
tr
ad
itio
n
al
ap
p
r
o
ac
h
es,
s
u
ch
as
m
a
n
u
al
teac
h
er
o
b
s
er
v
atio
n
o
r
test
-
b
ased
ev
alu
atio
n
s
,
wh
ich
ar
e
o
f
ten
s
u
b
jectiv
e,
tim
e
-
co
n
s
u
m
in
g
,
an
d
f
ail
to
ca
p
tu
r
e
p
s
y
ch
o
lo
g
ical
d
im
en
s
io
n
s
co
m
p
r
eh
e
n
s
iv
ely
[
6
]
–
[
8
]
.
T
h
is
lim
itatio
n
d
elay
s
ef
f
ec
tiv
e
in
ter
v
en
tio
n
s
an
d
ca
n
e
v
en
in
c
r
ea
s
e
s
tu
d
en
ts
’
p
s
y
ch
o
lo
g
ical
b
u
r
d
e
n
.
E
d
u
ca
tio
n
al
p
s
y
ch
o
l
o
g
y
r
esear
ch
em
p
h
asizes
th
at
in
tr
in
s
ic
m
o
tiv
atio
n
f
o
s
ter
s
p
er
s
is
ten
ce
,
s
t
r
o
n
g
i
n
t
e
r
e
s
t
i
m
p
r
o
v
e
s
e
n
g
a
g
e
m
e
n
t
,
w
h
i
l
e
a
n
x
i
e
t
y
r
e
d
u
c
e
s
c
o
n
c
e
n
t
r
a
t
i
o
n
a
n
d
a
c
a
d
e
m
i
c
p
e
r
f
o
r
m
a
n
c
e
[
9
]
–
[
1
2
]
.
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
n
a
r
tifi
cia
l n
eu
r
a
l n
etw
o
r
k
-
b
a
s
ed
d
ec
is
io
n
s
u
p
p
o
r
t m
o
d
el
f
o
r
ea
r
ly
p
r
ed
ictio
n
…
(
Ha
r
r
y
Dh
ika
)
619
Stu
d
en
ts
with
lo
w
m
o
tiv
ati
o
n
o
f
ten
s
tr
u
g
g
le
with
in
teg
r
al
co
n
ce
p
ts
[
1
3
]
,
wh
er
ea
s
th
o
s
e
with
h
ig
h
an
x
iety
f
r
eq
u
e
n
tly
ex
p
er
ien
ce
d
if
f
icu
lties
in
s
o
lv
in
g
tr
ig
o
n
o
m
etr
ic
p
r
o
b
lem
s
[
1
4
]
,
[
1
5
]
.
T
h
er
e
f
o
r
e,
p
s
y
ch
o
l
o
g
ical
in
d
icato
r
s
s
u
ch
as
m
o
tiv
atio
n
,
in
ter
est,
an
d
an
x
iety
a
r
e
ess
en
tial
f
o
r
u
n
d
er
s
tan
d
in
g
s
tu
d
en
ts
’
lear
n
in
g
d
if
f
icu
lties
in
m
ath
em
atics
[
1
6
]
.
L
ik
ewise,
s
tu
d
en
ts
with
lo
w
in
ter
est
ten
d
to
b
e
less
en
g
ag
ed
i
n
lear
n
in
g
ac
tiv
ities
[
1
7
]
.
Mo
s
t
ex
is
tin
g
ac
ad
em
ic
p
r
ed
ictio
n
s
y
s
tem
s
r
ely
s
o
lely
o
n
q
u
an
titativ
e
d
ata
lik
e
test
s
co
r
es
an
d
atten
d
an
ce
,
n
eg
lectin
g
p
s
y
ch
o
lo
g
ical
f
ac
to
r
s
[
1
8
]
,
[
1
9
]
.
T
h
is
lim
itatio
n
cr
ea
tes
a
s
ig
n
if
ican
t
g
ap
in
u
n
d
er
s
tan
d
i
n
g
th
e
r
o
o
t
ca
u
s
es
o
f
lear
n
in
g
d
if
f
icu
lties
,
as
ap
p
r
o
ac
h
es
in
teg
r
atin
g
b
o
th
ac
ad
em
ic
an
d
p
s
y
ch
o
lo
g
ical
in
d
icato
r
s
v
ia
a
s
tr
u
ctu
r
ed
d
ata
m
in
in
g
f
r
am
e
wo
r
k
an
d
ar
tific
ial
n
e
u
r
al
n
et
wo
r
k
s
(
ANN)
r
em
ain
cr
itically
lim
ited
,
p
ar
ticu
lar
ly
with
in
th
e
I
n
d
o
n
esian
e
d
u
ca
tio
n
al
co
n
tex
t.
T
o
a
d
d
r
ess
th
is
n
ee
d
,
th
is
s
tu
d
y
p
r
o
p
o
s
es
an
ANN
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b
ased
m
u
lticlas
s
m
o
d
el
to
ca
p
tu
r
e
c
o
m
p
lex
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n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
b
etwe
en
th
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v
ar
iab
les,
class
if
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g
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tu
d
e
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ts
in
to
ea
s
y
,
m
o
d
e
r
ate,
an
d
d
if
f
icu
lt
lear
n
i
n
g
le
v
els
[
2
0
]
,
[
2
1
]
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
o
f
th
is
wo
r
k
is
b
r
id
g
in
g
th
is
g
ap
b
y
in
te
g
r
at
in
g
ac
ad
em
ic
p
er
f
o
r
m
a
n
ce
with
co
m
p
r
eh
e
n
s
iv
e
p
s
y
ch
o
lo
g
ical
d
im
e
n
s
io
n
s
(
m
o
tiv
atio
n
,
an
x
iety
,
in
te
r
est,
s
el
f
-
co
n
f
i
d
en
ce
,
an
d
p
ar
en
tal
s
u
p
p
o
r
t)
.
Fu
r
th
e
r
m
o
r
e
,
th
e
m
o
d
el
ap
p
lies
th
e
c
r
o
s
s
-
in
d
u
s
tr
y
s
tan
d
a
r
d
p
r
o
ce
s
s
f
o
r
d
ata
m
in
in
g
(
C
R
I
SP
-
DM
)
f
r
am
ewo
r
k
em
b
ed
d
e
d
with
s
y
n
th
etic
m
in
o
r
ity
o
v
er
-
s
am
p
lin
g
tech
n
iq
u
e
(
SMOT
E
)
,
d
ata
au
g
m
en
tatio
n
,
an
d
f
o
c
al
lo
s
s
to
m
ax
im
ize
p
r
ed
ictio
n
r
o
b
u
s
tn
ess
.
T
o
th
e
b
est
o
f
o
u
r
k
n
o
wled
g
e,
th
is
is
o
n
e
o
f
th
e
f
ir
s
t
s
tu
d
ies
in
I
n
d
o
n
esia
to
co
m
b
in
e
th
ese
co
m
p
r
eh
e
n
s
iv
e
f
ac
to
r
s
with
in
an
ANN
f
r
am
ewo
r
k
f
o
r
ea
r
ly
lear
n
in
g
d
if
f
icu
lty
p
r
ed
i
ctio
n
.
T
h
e
ANN
o
p
e
r
ates
th
r
o
u
g
h
in
ter
co
n
n
ec
te
d
n
e
u
r
o
n
s
th
at
tr
an
s
m
it
in
p
u
t
d
ata
an
d
ad
ju
s
t
co
n
n
ec
tio
n
weig
h
ts
iter
ativ
ely
u
s
in
g
b
ac
k
p
r
o
p
ag
atio
n
a
n
d
g
r
ad
ien
t
d
escen
t
alg
o
r
ith
m
s
to
m
in
im
iz
e
p
r
ed
ictio
n
e
r
r
o
r
s
[
2
2
]
–
[
2
4
]
.
E
m
p
lo
y
in
g
n
o
n
lin
ea
r
ac
tiv
atio
n
f
u
n
ctio
n
s
s
u
ch
as
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
e
L
U
)
an
d
So
f
tm
ax
,
ANN
ef
f
ec
tiv
ely
ca
p
tu
r
es
co
m
p
lex
p
atter
n
s
an
d
p
er
f
o
r
m
s
m
u
lticlas
s
c
lass
if
icatio
n
,
in
clu
d
in
g
g
r
o
u
p
in
g
s
tu
d
en
ts
in
to
ea
s
y
,
m
o
d
er
ate,
an
d
d
if
f
ic
u
lt
lear
n
i
n
g
le
v
els.
I
ts
f
lex
ib
ilit
y
in
p
r
o
ce
s
s
in
g
v
ar
io
u
s
d
ata
ty
p
es,
n
u
m
er
ical,
ca
teg
o
r
ical,
an
d
b
e
h
av
io
r
al,
e
n
ab
les
ANN
to
co
m
b
in
e
ac
ad
em
ic
an
d
p
s
y
ch
o
l
o
g
ical
f
ac
to
r
s
with
in
o
n
e
an
aly
tical
m
o
d
el
[
2
5
]
.
Alth
o
u
g
h
ANN
r
is
k
o
v
e
r
f
itti
n
g
an
d
class
im
b
alan
ce
f
r
o
m
lim
ited
d
ata,
th
is
s
tu
d
y
ad
d
r
ess
es
th
ese
ch
allen
g
es
u
s
in
g
SMOT
E
,
d
ata
au
g
m
e
n
tatio
n
,
f
o
ca
l
lo
s
s
,
ea
r
ly
s
to
p
p
in
g
,
a
n
d
lea
r
n
in
g
r
ate
tu
n
in
g
.
T
h
e
r
esear
ch
f
o
llo
ws
th
e
s
i
x
-
s
tag
e
C
R
I
SP
-
DM
f
r
am
ewo
r
k
s
p
an
n
in
g
f
r
o
m
b
u
s
in
ess
u
n
d
er
s
tan
d
in
g
t
o
d
ep
lo
y
m
e
n
t
to
g
u
i
d
e
th
e
p
r
e
d
ictiv
e
m
o
d
elin
g
p
r
o
ce
s
s
.
Un
lik
e
p
r
io
r
s
tu
d
ies
th
at
f
o
cu
s
p
r
im
ar
ily
o
n
ac
ad
em
ic
p
er
f
o
r
m
an
ce
[
2
6
]
–
[
2
8
]
,
th
is
wo
r
k
in
teg
r
ates
b
o
th
ac
a
d
em
ic
an
d
p
s
y
ch
o
lo
g
ical
f
ac
to
r
s
with
i
n
th
e
ANN
-
C
R
I
SP
-
DM
p
ip
elin
e
[
2
8
]
–
[
3
2
]
f
o
r
m
u
lticlas
s
p
r
ed
ictio
n
o
f
m
ath
em
atics
lear
n
in
g
d
if
f
icu
lties
(
E
asy
,
Mo
d
e
r
ate,
Dif
f
icu
lt).
T
o
th
e
b
est
o
f
o
u
r
k
n
o
wled
g
e,
th
is
is
am
o
n
g
th
e
f
ir
s
t
s
tu
d
ies
in
I
n
d
o
n
esia
to
ad
o
p
t th
is
co
m
b
in
ed
a
p
p
r
o
ac
h
f
o
r
ea
r
ly
ed
u
ca
tio
n
al
d
ec
is
i
o
n
s
u
p
p
o
r
t.
T
h
e
p
r
o
p
o
s
ed
web
-
b
ased
s
y
s
tem
tar
g
ets
p
er
f
o
r
m
an
ce
b
en
ch
m
ar
k
s
o
f
at
least
8
5
%
ac
cu
r
ac
y
,
8
0
%
r
ec
all,
an
d
0
.
8
0
F1
-
s
co
r
e
to
en
ab
le
ea
r
ly
i
n
ter
v
en
tio
n
.
ANN
is
s
p
ec
if
ically
s
elec
ted
f
o
r
its
s
u
p
er
io
r
ab
ilit
y
to
m
o
d
el
co
m
p
le
x
,
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
with
in
h
eter
o
g
e
n
eo
u
s
ed
u
ca
tio
n
al
d
ata,
o
u
tp
e
r
f
o
r
m
in
g
tr
ad
itio
n
al
m
eth
o
d
s
lik
e
d
ec
is
io
n
tr
ee
s
(
DT
)
a
n
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
i
n
ca
p
tu
r
in
g
in
ter
ac
tio
n
s
b
etwe
en
co
g
n
itiv
e
an
d
a
f
f
ec
ti
v
e
f
ac
to
r
s
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
ad
o
p
ts
a
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
u
s
in
g
an
ANN
to
p
r
e
d
ict
s
tu
d
e
n
ts
’
m
a
th
em
atics
lear
n
in
g
d
if
f
icu
lty
lev
els
b
a
s
ed
o
n
ac
ad
em
ic
an
d
p
s
y
ch
o
lo
g
ical
in
d
icato
r
s
.
T
h
e
AN
N
m
o
d
el
class
if
ies
s
tu
d
en
ts
in
to
th
r
ee
ca
teg
o
r
ies:
E
asy
,
m
o
d
er
ate,
an
d
d
if
f
icu
lt
,
s
er
v
in
g
as
an
ea
r
ly
war
n
in
g
s
y
s
tem
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u
p
p
o
r
t
tim
ely
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u
ca
tio
n
al
in
ter
v
e
n
tio
n
s
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T
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e
o
v
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r
all
m
eth
o
d
o
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in
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d
el
tr
ain
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g
,
an
d
ev
alu
atio
n
,
is
illu
s
tr
ated
in
Fig
u
r
es 1
an
d
2
.
Fig
u
r
e
1
.
C
o
n
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p
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al
m
eth
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d
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I
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620
Du
r
in
g
tr
ain
in
g
,
d
ata
f
r
o
m
S
MA
Mu
h
am
m
ad
iy
ah
1
6
J
ak
a
r
ta
-
co
m
p
r
is
in
g
ac
a
d
em
ic
v
ar
i
ab
les
an
d
p
s
y
ch
o
lo
g
ical
f
ac
to
r
s
f
r
o
m
a
v
alid
ated
5
-
p
o
in
t
L
i
k
er
t
q
u
est
io
n
n
air
e
-
was
p
r
e
p
r
o
ce
s
s
ed
u
s
in
g
n
o
r
m
aliza
tio
n
,
ca
teg
o
r
ical
en
co
d
in
g
,
a
n
d
SM
OT
E
,
T
h
e
ANN
m
o
d
el
was
d
ev
elo
p
ed
v
ia
T
en
s
o
r
Flo
w/Ker
as
[
3
1
]
u
s
in
g
R
eL
U
ac
tiv
atio
n
[
3
2
]
,
d
r
o
p
o
u
t
r
eg
u
l
ar
izatio
n
,
a
n
d
th
e
A
d
am
o
p
ti
m
izer
with
t
u
n
ed
h
y
p
er
p
a
r
am
eter
s
.
Fo
r
e
v
alu
atio
n
,
3
0
%
o
f
t
h
e
d
ataset
was
r
es
er
v
ed
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test
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d
ata
to
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e
asu
r
e
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
e
ca
ll,
an
d
F1
-
s
co
r
e.
T
h
e
co
m
p
lete
li
s
t o
f
i
n
p
u
t a
ttri
b
u
tes is
s
u
m
m
ar
ized
in
T
ab
le
1
.
Fig
u
r
e
2
.
R
esear
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m
eth
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d
T
ab
le
1
.
I
n
p
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t
v
ar
iab
les
No
V
a
r
i
a
b
l
e
D
e
scri
p
t
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o
n
1
A
g
e
S
t
u
d
e
n
t
’
s
a
g
e
2
La
t
e
st
M
a
t
h
e
m
a
t
i
c
s Q
u
i
z
S
c
o
r
e
S
c
o
r
e
o
b
t
a
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n
e
d
f
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o
m t
h
e
m
o
st
r
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c
e
n
t
q
u
i
z
3
M
a
t
h
e
ma
t
i
c
s
S
e
m
e
st
e
r
E
x
a
m
S
c
o
r
e
F
i
n
a
l
e
x
a
m res
u
l
t
i
n
ma
t
h
e
m
a
t
i
c
s
4
M
a
t
h
e
ma
t
i
c
s As
si
g
n
me
n
t
S
c
o
r
e
A
v
e
r
a
g
e
sc
o
r
e
o
f
m
a
t
h
e
m
a
t
i
c
s
a
ssi
g
n
men
t
s
5
N
u
mb
e
r
o
f
M
a
t
h
e
m
a
t
i
c
s
R
e
me
d
i
a
l
S
e
ssi
o
n
s
F
r
e
q
u
e
n
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y
o
f
r
e
me
d
i
a
l
p
a
r
t
i
c
i
p
a
t
i
o
n
6
A
v
e
r
a
g
e
S
c
o
r
e
o
f
O
t
h
e
r
S
u
b
j
e
c
t
s
O
v
e
r
a
l
l
a
c
a
d
e
mi
c
a
c
h
i
e
v
e
me
n
t
a
c
r
o
ss
su
b
j
e
c
t
s
7
M
o
t
i
v
a
t
i
o
n
Le
v
e
l
o
f
e
n
t
h
u
s
i
a
sm
a
n
d
d
r
i
v
e
t
o
l
e
a
r
n
m
a
t
h
e
m
a
t
i
c
s
8
A
n
x
i
e
t
y
Le
v
e
l
o
f
a
n
x
i
e
t
y
d
u
r
i
n
g
p
r
o
b
l
e
m
-
so
l
v
i
n
g
9
I
n
t
e
r
e
st
I
n
t
e
r
e
st
a
n
d
c
u
r
i
o
si
t
y
t
o
w
a
r
d
ma
t
h
e
m
a
t
i
c
s
10
S
e
l
f
C
o
n
f
i
d
e
n
c
e
C
o
n
f
i
d
e
n
c
e
i
n
c
o
mp
l
e
t
i
n
g
ma
t
h
e
m
a
t
i
c
a
l
t
a
s
k
s
11
P
a
r
e
n
t
a
l
S
u
p
p
o
r
t
F
a
mi
l
y
e
n
c
o
u
r
a
g
e
me
n
t
i
n
a
c
a
d
e
m
i
c
l
e
a
r
n
i
n
g
12
S
t
u
d
y
D
u
r
a
t
i
o
n
A
v
e
r
a
g
e
t
i
me
s
p
e
n
t
st
u
d
y
i
n
g
ma
t
h
e
m
a
t
i
c
s
p
e
r
d
a
y
13
O
n
l
i
n
e
P
l
a
t
f
o
r
m A
c
c
e
ss Fr
e
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class
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m
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g
d
i
f
f
ic
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lty
in
to
th
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tar
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b
ased
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f
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q
u
izze
s
,
s
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ex
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s
,
an
d
ass
ig
n
m
en
ts
.
Du
r
in
g
tr
ain
in
g
,
th
e
ANN
m
o
d
el
m
ap
s
co
m
p
lex
r
elatio
n
s
h
ip
s
b
etwe
en
1
6
in
p
u
t
v
ar
iab
les
an
d
th
ese
d
if
f
icu
lty
lev
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in
teg
r
atin
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B
atch
No
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m
aliza
tio
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f
o
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f
aster
co
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g
en
ce
an
d
f
o
ca
l
lo
s
s
f
o
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cla
s
s
im
b
alan
ce
.
E
v
alu
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n
v
ia
co
n
f
u
s
io
n
m
at
r
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an
d
ep
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wis
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p
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m
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f
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tifie
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d
if
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b
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b
alan
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g
ac
ad
em
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ac
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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
n
a
r
tifi
cia
l n
eu
r
a
l n
etw
o
r
k
-
b
a
s
ed
d
ec
is
io
n
s
u
p
p
o
r
t m
o
d
el
f
o
r
ea
r
ly
p
r
ed
ictio
n
…
(
Ha
r
r
y
Dh
ika
)
621
T
ab
le
2
.
Ou
tp
u
t
v
ar
ia
b
le
class
i
f
icatio
n
C
l
a
s
s
S
c
o
r
e
R
a
n
g
e
D
e
scri
p
t
i
o
n
D
i
f
f
i
c
u
l
t
<
6
0
S
t
u
d
e
n
t
s
e
x
p
e
r
i
e
n
c
e
s
i
g
n
i
f
i
c
a
n
t
d
i
f
f
i
c
u
l
t
y
i
n
u
n
d
e
r
s
t
a
n
d
i
n
g
ma
t
h
e
ma
t
i
c
a
l
c
o
n
c
e
p
t
s.
M
o
d
e
r
a
t
e
60
–
79
S
t
u
d
e
n
t
s
sh
o
w
m
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d
e
r
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t
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c
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mp
r
e
h
e
n
s
i
o
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b
u
t
st
i
l
l
r
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q
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i
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m
p
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v
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me
n
t
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Ea
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≥
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S
t
u
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t
s
d
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m
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t
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y
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f
mat
h
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mat
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ma
t
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i
a
l
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
f
in
d
in
g
s
o
f
th
e
s
tu
d
y
,
in
clu
d
in
g
d
ata
ex
p
lo
r
ati
o
n
r
esu
lts
,
ANN
m
o
d
el
ev
alu
atio
n
,
a
n
d
p
r
ed
ictiv
e
an
aly
s
is
r
elate
d
to
th
e
class
if
icatio
n
o
f
s
tu
d
en
ts
’
m
ath
em
atics
lear
n
in
g
d
if
f
icu
lty
lev
els.
T
h
e
d
ata
ex
p
lo
r
atio
n
s
tag
e
p
r
o
v
id
es
an
o
v
e
r
v
iew
o
f
th
e
d
ataset’
s
s
tr
u
ctu
r
e
an
d
d
is
tr
ib
u
tio
n
,
as
well
as
th
e
r
elatio
n
s
h
ip
b
etwe
en
ac
ad
em
ic
an
d
p
s
y
ch
o
lo
g
i
ca
l
in
d
icato
r
s
an
d
th
e
ca
t
eg
o
r
ized
lev
els
o
f
lear
n
in
g
d
i
f
f
icu
lty
.
3
.
1
.
Da
t
a
s
et
o
v
er
v
iew
T
h
e
p
r
ep
r
o
ce
s
s
ed
d
ataset
co
n
s
is
ts
o
f
1
2
0
s
tu
d
en
t
r
ec
o
r
d
s
with
a
tar
g
et
v
ar
iab
le
d
is
tr
ib
u
ted
in
to
th
r
ee
class
es
:
Mo
d
er
ate
(
4
6
.
6
7
%),
E
asy
(
2
9
.
1
7
%),
a
n
d
Dif
f
i
cu
lt
(
2
4
.
1
7
%),
in
d
icatin
g
a
m
an
ag
ea
b
le
class
im
b
alan
ce
.
Descr
ip
tiv
e
s
tatis
ti
cs
r
ev
ea
l
th
at
th
e
m
ea
n
a
s
s
ig
n
m
en
t
s
co
r
e
(
8
2
.
6
7
±
1
3
.
4
3
)
is
n
o
tab
ly
h
ig
h
er
t
h
an
th
e
s
em
ester
ex
am
(
6
2
.
7
6
±
1
8
.
0
0
)
a
n
d
d
aily
test
(
6
6
.
5
5
±
1
8
.
7
1
)
s
co
r
es,
wh
ile
r
em
ed
ial
f
r
eq
u
en
cy
s
h
o
ws
h
ig
h
v
ar
iab
ilit
y
(
9
.
1
3
±
2
2
.
7
7
)
.
Stu
d
en
ts
p
er
f
o
r
m
b
etter
in
n
o
n
-
m
at
h
em
atics
s
u
b
jects,
as
s
h
o
wn
b
y
th
e
h
ig
h
av
er
ag
e
s
co
r
e
o
f
o
t
h
er
c
o
u
r
s
es
(
7
9
.
3
7
±
1
3
.
8
0
)
.
Alth
o
u
g
h
s
ev
er
al
p
s
y
ch
o
lo
g
ical
v
ar
ia
b
les
wer
e
ex
clu
d
ed
d
u
r
in
g
in
te
g
r
atio
n
d
u
e
to
in
co
m
p
lete
r
esp
o
n
s
es,
th
e
f
in
al
d
ataset
ef
f
ec
tiv
ely
ca
p
tu
r
es
h
eter
o
g
en
eo
u
s
ac
ad
em
ic
p
er
f
o
r
m
an
ce
p
atter
n
s
,
m
ak
in
g
it h
ig
h
ly
s
u
itab
le
f
o
r
r
o
b
u
s
t m
u
lticlas
s
cla
s
s
if
icatio
n
m
o
d
elin
g
.
3
.
2
.
Da
t
a
e
x
plo
ra
t
io
n
Data
ex
p
lo
r
atio
n
r
ev
ea
ls
th
at
m
o
s
t
s
tu
d
en
ts
f
all
in
to
th
e
m
o
d
e
r
ate
lear
n
in
g
ch
allen
g
e
ca
teg
o
r
y
(
s
co
r
es
6
0
–
7
9
)
,
with
s
em
ester
ex
am
s
an
d
r
em
ed
ial
f
r
e
q
u
en
cy
s
tr
o
n
g
ly
in
f
lu
en
cin
g
d
if
f
icu
lty
lev
els.
I
n
p
u
t
d
is
tr
ib
u
tio
n
s
v
ar
y
,
s
h
o
win
g
n
o
r
m
ally
d
is
tr
ib
u
ted
d
a
ily
q
u
izze
s
alo
n
g
s
id
e
u
n
ev
en
ly
d
is
p
er
s
ed
s
tu
d
y
tim
es
an
d
d
iv
er
s
e
p
s
y
c
h
o
lo
g
i
ca
l
r
ea
d
in
ess
(
m
o
tiv
atio
n
an
d
an
x
iety
)
.
Dis
tin
ct
to
p
ic
-
s
p
ec
if
ic
p
atter
n
s
em
er
g
e
:
h
ig
h
an
x
iet
y
h
am
p
e
r
s
tr
ig
o
n
o
m
etr
y
p
er
f
o
r
m
an
ce
(
esp
ec
ially
in
wo
r
d
p
r
o
b
lem
s
)
,
w
ea
k
an
tid
er
iv
ativ
e
co
m
p
r
eh
e
n
s
io
n
h
i
n
d
er
s
ca
lcu
l
u
s
/in
teg
r
als,
co
m
p
lex
e
q
u
atio
n
s
d
is
r
u
p
t
alg
eb
r
a
,
an
d
lo
w
v
is
u
aliza
tio
n
lim
its
s
o
lid
g
eo
m
etr
y
.
Ultim
ately
,
th
ese
f
in
d
in
g
s
co
n
f
ir
m
a
c
r
itical
in
ter
r
ela
tio
n
s
h
ip
wh
er
e
s
tr
o
n
g
m
o
tiv
atio
n
an
d
co
n
f
id
en
ce
en
a
b
le
co
n
s
is
ten
t
p
r
o
b
lem
-
s
o
lv
in
g
,
wh
ile
p
s
y
ch
o
lo
g
ical
f
ac
to
r
s
h
ea
v
ily
d
ictate
ac
ad
e
m
ic
m
ath
em
atica
l d
if
f
icu
lties
ac
r
o
s
s
s
p
ec
if
ic
to
p
ics.
3
.
3
.
M
o
del
dev
elo
pm
ent
a
nd
t
ra
ini
ng
T
h
e
class
if
icatio
n
m
o
d
el
in
th
is
s
tu
d
y
u
s
es
a
m
u
ltil
a
y
er
ANN
co
n
s
is
tin
g
o
f
d
e
n
s
e,
b
atch
n
o
r
m
aliza
tio
n
,
an
d
d
r
o
p
o
u
t
lay
er
s
to
r
ed
u
ce
o
v
er
f
itti
n
g
.
T
h
e
m
o
d
el
was
tr
ai
n
ed
u
s
in
g
d
ata
f
r
o
m
SMA
Mu
h
am
m
ad
iy
a
h
1
6
J
ak
ar
ta,
with
a
7
0
:
3
0
tr
ain
–
test
s
p
lit.
T
o
ad
d
r
ess
class
im
b
alan
ce
,
d
ata
au
g
m
e
n
tatio
n
an
d
th
e
SMOT
E
wer
e
ap
p
lied
b
ef
o
r
e
tr
ain
in
g
.
T
h
ese
tech
n
iq
u
es
g
en
er
ated
5
0
s
y
n
th
etic
s
am
p
les,
in
c
r
ea
s
in
g
th
e
d
ataset
f
r
o
m
1
1
3
to
1
6
3
r
ec
o
r
d
s
an
d
im
p
r
o
v
in
g
class
b
alan
ce
f
r
o
m
a
d
o
m
in
an
t
Mo
d
e
r
ate
class
(
5
3
s
am
p
les)
to
a
m
o
r
e
b
ala
n
ce
d
d
is
tr
ib
u
tio
n
o
f
8
3
Mo
d
e
r
ate,
4
3
E
asy
,
an
d
3
7
Dif
f
icu
lt sam
p
les ac
co
r
d
in
g
T
ab
le
3
.
T
ab
le
3
.
C
lass
d
is
tr
ib
u
tio
n
b
ef
o
r
e
an
d
af
ter
au
g
m
en
tatio
n
C
a
t
e
g
o
r
y
B
e
f
o
r
e
A
u
g
m
e
n
t
a
t
i
o
n
A
f
t
e
r
A
u
g
me
n
t
a
t
i
o
n
Ea
sy
33
43
M
o
d
e
r
a
t
e
53
83
D
i
f
f
i
c
u
l
t
27
37
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f
t
e
r
p
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p
r
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c
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s
s
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g
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t
h
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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J
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&
C
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m
p
Sci
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623
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ast,
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e
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in
g
le
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ain
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test
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lit
s
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at
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e
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N
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8
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r
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d
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F1
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s
co
r
e,
r
ep
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tin
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its
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est
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s
e
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f
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ile
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m
o
r
e
r
eliab
le
esti
m
ate
o
f
g
en
er
aliza
tio
n
ab
ilit
y
.
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
&
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o
m
p
Sci
,
Vo
l.
4
3
,
No
.
2
,
Au
g
u
s
t
20
2
6
:
6
1
8
-
6
2
7
624
B
ased
o
n
T
ab
le
6
,
th
e
ANN
o
u
tp
er
f
o
r
m
ed
all
b
aselin
e
m
o
d
e
ls
in
ac
cu
r
ac
y
an
d
F1
-
s
co
r
e,
co
n
f
ir
m
in
g
its
ab
ilit
y
to
ca
p
tu
r
e
co
m
p
lex
n
o
n
lin
ea
r
r
elatio
n
s
h
i
p
s
in
th
e
d
ata.
R
an
d
o
m
f
o
r
est
(
R
F)
s
h
o
wed
c
o
m
p
etitiv
e
p
er
f
o
r
m
an
ce
as
th
e
s
tr
o
n
g
est
tr
ad
itio
n
al
m
o
d
el,
wh
ile
DT
an
d
SVM
p
e
r
f
o
r
m
ed
l
o
wer
,
in
d
icatin
g
lim
ited
ab
ilit
y
to
m
o
d
el
c
o
m
p
lex
f
ea
tu
r
e
in
ter
ac
tio
n
s
.
3
.
5
.
F
e
a
t
ure
a
na
ly
s
is
B
ased
o
n
th
e
ANN
m
o
d
el's
f
ea
tu
r
e
im
p
o
r
tan
ce
(
Fig
u
r
e
7
)
,
a
ca
d
em
ic
p
er
f
o
r
m
an
ce
v
a
r
iab
le
s
em
er
g
e
as
th
e
d
o
m
in
an
t
p
r
ed
icto
r
s
o
f
m
ath
em
atics
lear
n
in
g
d
if
f
icu
lties
co
m
p
ar
ed
to
wea
k
er
b
eh
av
io
r
al
an
d
d
em
o
g
r
a
p
h
ic
f
ac
to
r
s
.
C
o
n
tin
u
o
u
s
an
d
s
u
m
m
ativ
e
ass
ess
m
en
ts
h
o
ld
th
e
h
ig
h
est
in
f
lu
e
n
ce
,
l
ed
b
y
th
e
last
m
ath
d
aily
test
s
co
r
e
(
0
.
3
0
2
)
an
d
th
e
m
at
h
s
em
ester
ex
a
m
s
c
o
r
e
(
0
.
2
7
8
)
,
f
o
llo
we
d
m
o
d
er
ately
b
y
th
e
m
at
h
ass
ig
n
m
en
t
s
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r
e
(
0
.
1
1
4
)
w
h
ich
r
ef
lects
f
o
r
m
ativ
e
c
o
n
s
i
s
ten
cy
.
I
n
co
n
tr
ast,
th
e
av
er
ag
e
s
co
r
e
o
f
o
th
er
s
u
b
jects
(
0
.
0
6
7
)
ex
e
r
ts
o
n
ly
a
s
ec
o
n
d
ar
y
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ad
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ic
in
f
l
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ce
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Am
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g
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ac
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ic
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ag
e
(
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0
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1
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p
r
o
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id
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a
m
in
o
r
d
ev
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p
m
e
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tal
co
n
tr
i
b
u
tio
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il
e
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h
e
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u
m
b
er
o
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m
ath
r
em
ed
ials
ta
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en
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h
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Ultim
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ese
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in
d
in
g
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co
n
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ir
m
th
at
t
h
e
m
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el
p
r
i
m
ar
ily
r
elies
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n
s
tr
u
ctu
r
ed
ac
ad
em
ic
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er
f
o
r
m
an
ce
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atter
n
s
to
class
if
y
lear
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g
d
if
f
icu
lty
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els.
Fig
u
r
e
7
.
Featu
r
e
i
m
p
o
r
ta
n
ce
3
.
6
.
P
r
a
ct
ica
l
i
m
pli
ca
t
io
ns
T
h
is
s
tu
d
y
c
o
n
tr
ib
u
tes
to
t
h
e
d
ev
elo
p
m
e
n
t
o
f
m
ac
h
in
e
lear
n
in
g
–
b
ased
d
ec
is
io
n
s
u
p
p
o
r
t
s
y
s
tem
s
in
ed
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ca
tio
n
b
y
d
em
o
n
s
tr
atin
g
th
e
ef
f
ec
tiv
e
n
ess
o
f
ANN
in
p
r
e
d
ictin
g
s
tu
d
e
n
ts
’
lear
n
i
n
g
d
if
f
icu
lties
u
s
in
g
ac
ad
em
ic
an
d
p
s
y
ch
o
lo
g
ical
d
ata.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
en
ab
les
ea
r
ly
id
e
n
tific
atio
n
o
f
at
-
r
is
k
s
tu
d
e
n
ts
,
s
u
p
p
o
r
tin
g
tim
ely
in
ter
v
en
tio
n
s
an
d
r
ed
u
cin
g
r
elian
ce
o
n
s
u
b
jectiv
e
ass
ess
m
en
t
m
eth
o
d
s
.
I
t
ca
n
also
ass
i
s
t
ed
u
ca
tio
n
al
au
th
o
r
ities
in
m
a
p
p
in
g
lear
n
in
g
d
if
f
icu
lties
at
a
b
r
o
ad
er
s
ca
le,
alth
o
u
g
h
its
ap
p
licatio
n
s
h
o
u
ld
co
n
s
id
er
d
ata
p
r
iv
ac
y
,
m
o
d
el
v
alid
ity
,
an
d
eth
ical
u
s
e.
3
.
7
.
Str
eng
t
hs
a
nd
l
im
it
a
t
io
ns
T
h
is
s
tu
d
y
p
r
esen
ts
a
s
y
s
tem
atic
ANN
-
b
ased
f
r
am
ewo
r
k
i
n
teg
r
atin
g
ac
a
d
em
ic
an
d
p
s
y
ch
o
lo
g
ical
d
ata;
h
o
wev
er
,
its
f
in
d
in
g
s
ar
e
lim
ited
b
y
a
r
elativ
ely
s
m
all,
s
in
g
le
-
in
s
titu
tio
n
d
ataset
an
d
p
o
ten
tial
b
ias
f
r
o
m
s
elf
-
r
ep
o
r
ted
i
n
f
o
r
m
atio
n
.
4.
CO
NCLU
SI
O
N
T
h
is
s
t
u
d
y
d
e
v
el
o
p
e
d
a
n
AN
N
-
b
ase
d
m
o
d
el
wit
h
i
n
th
e
C
R
I
S
P
-
DM
f
r
a
m
e
wo
r
k
t
o
p
r
e
d
i
ct
m
ath
em
ati
cs
lea
r
n
i
n
g
d
if
f
i
cu
lti
es,
ac
h
i
ev
in
g
9
8
% a
cc
u
r
a
cy
wit
h
b
ala
n
c
e
d
p
e
r
f
o
r
m
a
n
c
e
a
cr
o
s
s
all
le
v
e
ls
.
R
esu
l
ts
i
n
d
ic
ate
t
h
at
ac
a
d
e
m
i
c
i
n
d
ic
at
o
r
s
ar
e
t
h
e
m
o
s
t
i
n
f
lu
en
tia
l
p
r
ed
ict
o
r
s
,
s
u
p
p
o
r
ti
n
g
e
f
f
ec
ti
v
e
e
a
r
l
y
d
et
ec
ti
o
n
.
H
o
w
ev
er
,
th
e
s
t
u
d
y
is
li
m
ite
d
b
y
a
r
e
lat
iv
e
ly
s
m
al
l
d
atas
et
f
r
o
m
a
s
in
g
le
i
n
s
t
it
u
ti
o
n
a
n
d
p
o
te
n
t
ial
s
e
lf
-
r
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o
r
ti
n
g
b
ias
i
n
p
s
y
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h
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g
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ca
l
d
at
a.
F
u
t
u
r
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h
o
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ld
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x
p
a
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t
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d
at
as
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m
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r
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ati
v
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m
ac
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l
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r
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i
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g
ap
p
r
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h
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t
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n
h
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m
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e
l
g
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n
er
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za
t
io
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f
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r
r
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al
-
wo
r
l
d
e
d
u
ca
ti
o
n
al
a
p
p
li
ca
t
io
n
s
.
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
n
a
r
tifi
cia
l n
eu
r
a
l n
etw
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p
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t m
o
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el
f
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ly
p
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(
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r
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625
ACK
NO
WL
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DG
M
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h
e
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t
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Dir
ec
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ate
o
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R
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n
d
C
o
m
m
u
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ity
Ser
v
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PM)
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Min
is
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Un
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d
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h
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er
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ated
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ated
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alize
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ated
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ee
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ased
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fr
o
m
Un
i
v
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r
sitas
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a
d
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a
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a
(1
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)
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n
d
a
M
a
ste
r’s
in
C
o
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m
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ica
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t
u
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ies
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m
S
a
h
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d
Un
iv
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rsity
(
2
0
0
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).
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c
e
2
0
0
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,
h
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fu
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m
e
lec
tu
re
r
a
t
Un
i
v
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rsitas
In
d
ra
p
ra
sta
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RI,
wh
e
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tl
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a
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sila,
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d
Civ
ic
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u
c
a
ti
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n
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h
a
s
p
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sh
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m
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ro
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m
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it
ies
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n
d
c
u
lt
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ra
l
issu
e
s.
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u
ra
ji
y
o
c
a
n
b
e
c
o
n
tac
ted
v
ia em
a
il
a
t:
d
rss
u
ra
ji
y
o
@
g
m
a
il
.
c
o
m
.
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sia
A
g
u
stin
a
S
h
e
is
a
l
e
c
tu
re
r
a
t
Un
iv
e
rsitas
In
d
ra
p
ra
st
a
P
G
RI
Ja
k
a
rta
,
In
d
o
n
e
sia
.
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r
re
se
a
rc
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tere
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in
c
lu
d
e
a
lg
e
b
ra
,
tr
ig
o
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o
m
e
try
,
in
t
ro
d
u
c
to
ry
m
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th
e
m
a
ti
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s,
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n
d
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p
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m
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m
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ti
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s.
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h
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ti
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k
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n
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ro
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lem
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o
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lf
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e
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c
y
,
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n
d
APOS
lea
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,
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s
we
ll
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s
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o
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tri
b
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to
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c
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m
ic
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.
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r
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se
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rc
h
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lu
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stu
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ie
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o
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m
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a
ti
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l
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tera
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y
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c
re
a
ti
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th
in
k
in
g
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a
n
d
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stru
c
ti
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l
stra
teg
ies
.
S
h
e
c
a
n
b
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re
a
c
h
e
d
v
ia
e
m
a
il
:
las
iaa
g
u
stin
a
@g
m
a
il
.
c
o
m
.
Abd
u
l
Mu
c
h
l
is
h
e
re
c
e
iv
e
d
h
is
Ba
c
h
e
lo
r’s
d
e
g
re
e
fro
m
IKIP
P
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RI
S
e
m
a
ra
n
g
(2
0
0
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)
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n
d
h
is
M
a
ste
r’s
in
M
a
th
e
m
a
ti
c
s
fro
m
UH
AMKA
Ja
k
a
rta
(2
0
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)
.
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c
e
rti
fied
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r
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n
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rn
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fa
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il
it
a
to
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P
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t
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h
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m
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h
1
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n
d
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1
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Ja
k
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rta.
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rc
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m
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m
a
ti
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d
u
c
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ti
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n
,
in
str
u
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sig
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lea
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v
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ti
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.
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th
is
st
u
d
y
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h
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c
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n
tri
b
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ta
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ll
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d
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tas
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rd
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d
q
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stio
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ire i
n
terp
re
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o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
b
d
u
lm
u
c
h
li
s1
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m
a
il
.
c
o
m
.
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