I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
10
, N
o.
2
,
J
une
202
1
, pp.
324
~
331
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
10
.i
2
.pp
324
-
331
324
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
c
or
e
.c
om
A
c
om
p
ar
i
son
b
e
t
w
e
e
n
d
e
e
p
l
e
ar
n
i
n
g, n
aï
ve
b
aye
s an
d
r
an
d
om
f
or
e
st
f
or
t
h
e
ap
p
l
i
c
at
i
on
of
d
at
a m
i
n
i
n
g on
t
h
e
ad
m
i
ss
i
o
n
of
n
e
w
st
u
d
e
n
t
s
N
u
r
h
ac
h
it
a
1
, E
d
i
S
u
r
ya N
e
gar
a
2
1
Universitas Islam
Negeri Raden Fatah Pal
embang,
Indonesia
2
Universitas Bi
na Darma, Indonesia
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
F
e
b
2
1
, 20
20
R
e
vi
s
e
d
J
a
n 5,
20
21
A
c
c
e
pt
e
d
M
a
r
2
9
, 20
21
The
process
of
admitting
new
students
at
Universitas
Islam
Negeri
Raden
Fatah
each
year
produces
a
lot
of
new
student
data.
So
that
ther
e
is
an
accumulati
on
of
student
data
continu
ously.
The
purpose
of
this
study
is
to
compare
deep
learning,
naïve
bayes,
and
random
forest
on
the
admis
sion
of
new
students
as
well
as
being
one
of
the
bases
for
making
decis
ions
to
determine
the
promotion
strategy
of
each
study
program.
The
data
mining
method
used
is
knowledge
discovery
in
database
(KDD).
The
tools
u
sed
are
rapid
miner.
The
attributes
used
are
student
ID
number,
name,
p
rogram
study,
faculty,
gender,
place
of
birth,
date
of
birth,
year
of
entry,
school
origin,
national
examination,
type
of
payment,
and
nominal
payme
nt.
The
new
student
data
used
from
2016
to
2019
was
an
18.930
item.
The
re
sults
of
this
s
tudy
used
deep
learning
bayes
results
resulted
in
an
accuracy
v
alue
of
52.65%,
naïve
bayes
results
resulted
in
an
accuracy
value
of
99.79
%,
and
random forest
results res
ulted in
an accuracy value of
44.65%.
K
e
y
w
o
r
d
s
:
D
a
ta
m
in
in
g
D
e
e
p l
e
a
r
ni
ng
N
a
ïv
e
ba
ye
s
N
e
w
s
tu
de
nt
s
R
a
ndom f
or
e
s
t
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
E
di
S
ur
ya
N
e
ga
r
a
D
a
ta
S
c
ie
nc
e
I
nt
e
r
di
s
c
ip
li
na
r
y R
e
s
e
a
r
c
h C
e
nt
e
r
C
om
put
e
r
S
c
ie
nc
e
F
a
c
ul
ty
U
ni
ve
r
s
it
a
s
B
in
a
D
a
r
m
a
, I
ndone
s
ia
E
m
a
il
:
e
.s
.ne
ga
r
a
@
bi
na
da
r
m
a
.a
c
.i
d
1.
I
N
T
R
O
D
U
C
T
I
O
N
I
nf
or
m
a
ti
on
te
c
hnol
ogy
ha
s
a
n
im
por
ta
nt
r
ol
e
in
m
os
t
or
ga
ni
z
a
ti
on
th
a
t
m
a
ni
pul
a
te
s
a
nd
c
ol
le
c
ts
da
ta
in
la
r
ge
d
a
ta
ba
s
e
s
.
D
e
c
i
s
io
n
m
a
ki
ng
i
s
ge
ne
r
a
te
d
by
u
s
e
f
ul
in
f
or
m
a
ti
on
f
r
om
s
to
r
in
g
da
ta
.
D
a
ta
s
to
r
a
ge
pa
tt
e
r
ns
th
a
t
ha
ve
be
e
n
di
s
c
ove
r
e
d
by
us
e
r
s
to
he
lp
th
e
da
ta
a
n
a
ly
s
is
pr
oc
e
s
s
is
c
a
ll
e
d
da
ta
m
in
in
g
[
1]
.
A
lo
ng
w
it
h
th
e
de
ve
lo
pm
e
nt
of
th
e
in
te
r
ne
t,
th
e
da
ta
s
to
r
e
d,
bot
h
in
t
he
f
or
m
of
te
xt
,
im
a
ge
s
,
s
ound,
a
nd
vi
de
o
a
ls
o
in
c
r
e
a
s
e
d
ve
r
y
qui
c
kl
y
a
nd
s
ig
ni
f
ic
a
nt
ly
.
I
n
I
ndone
s
ia
,
in
te
r
ne
t
us
e
r
s
in
1998
w
e
r
e
onl
y
500,000
us
e
r
s
w
he
r
e
a
s
by
2015
it
w
a
s
pr
oj
e
c
te
d
th
a
t
in
te
r
ne
t
us
e
r
s
ha
d
r
e
a
c
he
d
139
m
il
li
on
[
2]
.
in
f
or
m
a
ti
on
c
a
n
id
e
nt
i
f
ie
d
by
c
ha
r
a
c
te
r
is
ti
c
s
[
3]
.
T
h
e
la
r
ge
vol
um
e
of
da
ta
vol
um
e
w
il
l
be
c
om
e
"
ga
r
ba
ge
"
in
s
to
r
a
ge
if
it
is
not
pr
oc
e
s
s
e
d
in
to
us
e
f
ul
in
f
or
m
a
ti
on.
D
a
ta
m
in
in
g
te
c
hnol
ogy
pr
ovi
de
s
a
us
e
r
-
or
ie
nt
e
d
a
ppr
oa
c
h
to
nove
l
a
nd
hi
dde
n
pa
tt
e
r
ns
in
th
e
da
ta
[
4]
.
M
ix
e
d
da
ta
m
ode
ls
th
a
t
ha
ve
m
a
ny
to
pi
c
s
c
a
n
f
or
m
a
te
xt
da
ta
s
e
t
m
ode
l
[
5]
.
T
hi
s
is
c
on
s
is
te
nt
w
it
h
th
e
de
f
in
it
io
n
of
da
ta
th
a
t
da
ta
is
a
f
a
c
t
th
a
t
is
r
e
c
or
de
d
but
ha
s
no
m
e
a
ni
ng.
M
a
ny
uni
ve
r
s
it
ie
s
ha
ve
u
s
e
d
in
f
or
m
a
ti
on
te
c
hnol
ogy
to
s
uppor
t
th
e
a
dm
is
s
io
n
pr
oc
e
s
s
[
6]
.
T
h
e
a
ppl
ic
a
ti
on
o
f
in
f
or
m
a
ti
on
te
c
hnol
ogy
to
e
duc
a
ti
on
c
a
n
a
ls
o
pr
oduc
e
a
bunda
nt
s
tu
de
nt
da
ta
a
nd
le
a
r
ni
ng
p
r
oc
e
s
s
e
s
[
7]
.
A
t
uni
ve
r
s
it
ie
s
, da
ta
c
a
n be
obt
a
in
e
d f
r
om
da
ta
ba
s
e
s
, da
ta
w
il
l
c
ont
in
ue
t
o gr
ow
, s
uc
h a
s
s
tu
de
nt
da
ta
. H
ope
a
f
te
r
th
is
da
ta
m
in
in
g t
e
c
hni
que
c
a
n be
u
s
e
d a
nd u
s
e
f
ul
a
nd he
lp
a
n
a
ly
z
e
da
ta
i
n hi
ghe
r
e
duc
a
ti
on i
ns
ti
tu
ti
ons
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
c
om
par
is
on be
tw
e
e
n de
e
p l
e
a
r
ni
ng, naïv
e
bay
e
s
and r
andom
fo
r
e
s
t
fo
r
t
he
appli
c
at
io
n of
…
(
N
u
r
hac
hi
ta
)
325
T
he
pr
oc
e
s
s
of
a
dm
it
ti
ng ne
w
s
tu
de
nt
s
a
t
U
ni
ve
r
s
it
a
s
I
s
la
m
N
e
ge
r
i
R
a
de
n F
a
ta
h e
ve
r
y ye
a
r
pr
oduc
e
s
a
lo
t
of
ne
w
s
tu
de
nt
da
t
a
.
T
hi
s
ha
pp
e
ns
c
ont
in
uous
ly
s
o
th
a
t
t
he
r
e
is
a
n
a
c
c
um
ul
a
ti
on
of
s
tu
de
nt
d
a
ta
w
hi
c
h
w
il
l
c
ont
in
uous
ly
i
nc
r
e
a
s
e
i
n t
he
s
e
a
r
c
h f
or
s
tu
de
nt
i
nf
o
r
m
a
ti
on
. B
a
s
e
d on the
a
m
ount
of
ne
w
s
tu
de
nt
da
ta
, by
m
a
na
gi
ng
th
e
da
ta
,
in
f
or
m
a
ti
on
th
a
t
c
a
n
be
s
e
e
n
c
a
n
be
done
by
th
e
U
ni
ve
r
s
it
y.
B
a
s
e
d
on
th
e
num
be
r
of
ne
w
s
tu
de
nt
da
ta
,
by
or
g
a
ni
z
in
g
th
e
da
ta
s
o
th
a
t
in
f
or
m
a
ti
on
c
a
n
b
e
a
c
c
e
s
s
e
d
a
nd
a
c
c
e
pt
e
d
by
th
e
uni
ve
r
s
it
y,
f
or
e
xa
m
pl
e
,
a
c
om
pi
la
ti
on
of
uni
ve
r
s
it
y
pr
om
ot
io
ns
or
out
r
e
a
c
h
a
nd
s
tu
dy
pr
ogr
a
m
s
in
s
c
hool
s
to
a
c
c
e
pt
ne
w
s
tu
de
nt
s
,
uni
ve
r
s
it
ie
s
a
c
c
e
s
s
s
c
hool
s
f
or
pr
om
ot
io
n.
T
hi
s
c
a
u
s
e
s
a
w
a
s
te
of
budge
t
be
c
a
u
s
e
to
o
m
a
ny
s
c
hool
s
w
il
l
be
vi
s
it
e
d,
a
nd
not
ti
m
e
e
f
f
ic
ie
nt
.
T
hi
s
r
e
s
e
a
r
c
h
w
il
l
c
l
a
s
s
i
f
y
a
n
d
c
l
a
r
if
y
d
a
t
a
o
n
t
h
e
a
dm
i
s
s
i
o
n
o
f
n
e
w
s
t
u
d
e
n
t
s
a
t
U
n
iv
e
r
s
i
t
a
s
I
s
l
a
m
N
e
g
e
r
i
R
a
d
e
n
F
a
t
a
h
b
y
u
ti
l
i
z
in
g
t
he
d
a
t
a
m
i
n
i
ng
p
r
o
c
e
s
s
b
y
a
p
pl
y
i
ng
c
l
a
r
i
f
i
c
a
t
i
o
n
t
e
c
h
n
i
q
u
e
s
.
B
y
c
o
m
p
a
r
i
n
g
t
h
e
t
h
r
e
e
a
l
g
or
i
t
hm
s
,
d
e
e
p
l
e
a
r
n
in
g
,
n
a
ïv
e
b
a
y
e
s
,
a
n
d
r
a
n
d
om
f
or
e
s
t
.
T
h
e
t
o
o
l
s
u
s
e
d
a
r
e
r
a
p
i
d
m
i
n
e
r
.
T
h
e
a
t
tr
i
b
ut
e
s
u
s
e
d
a
r
e
s
tu
d
e
n
t
I
D
n
um
b
e
r
,
n
a
m
e
,
pr
o
g
r
a
m
s
tu
d
y
,
f
a
c
u
l
ty
,
p
l
a
c
e
o
f
bi
r
th
,
g
e
n
d
e
r
,
d
a
t
e
o
f
b
ir
t
h
,
s
c
h
o
ol
o
r
i
g
i
n
,
y
e
a
r
o
f
e
nt
r
y,
n
a
t
i
on
a
l
e
x
a
m
in
a
t
i
on
,
t
y
p
e
of
p
a
y
m
e
n
t,
a
n
d
n
o
m
i
n
a
l
p
a
ym
e
n
t
.
B
a
s
e
d
o
n
t
h
e
r
e
s
u
l
t
s
o
f
d
e
e
p
l
e
a
r
n
i
n
g,
n
a
ï
v
e
b
a
y
e
s
,
a
n
d
r
a
n
do
m
f
or
e
s
t
c
a
n
de
t
e
r
m
in
e
th
e
p
r
om
o
t
io
n
s
t
r
a
t
e
gy
o
f
e
a
c
h
s
tu
d
y
p
r
o
gr
a
m
.
B
a
s
e
d
o
n
t
h
e
r
e
s
u
l
t
s
of
d
e
e
p
l
e
a
r
n
i
n
g,
n
a
ï
v
e
b
a
y
e
s
,
a
n
d
r
a
n
d
o
m
f
or
e
s
t
c
a
n
s
e
e
c
o
u
r
s
e
s
o
f
i
n
t
e
r
e
s
t
i
n
e
a
c
h
s
c
h
o
o
l
.
T
h
e
f
i
n
a
l
r
e
s
u
lt
s
o
f
t
h
e
c
lu
s
t
e
r
c
a
n
h
e
l
p
t
h
e
U
ni
ve
r
s
it
y [
8
]
.
D
a
ta
m
in
in
g
c
onc
e
pt
is
to
e
xt
r
a
c
t
hi
dde
n
pa
tt
e
r
ns
a
nd
to
di
s
c
ove
r
r
e
la
ti
ons
hi
ps
be
twe
e
n
pa
r
a
m
e
te
r
s
in
a
va
s
t
a
m
ount
of
da
ta
[
9]
.
D
a
ta
m
in
in
g
is
th
e
pr
oc
e
s
s
of
e
xt
r
a
c
ti
ng
da
ta
(
pr
e
vi
ous
ly
unknown,
im
pl
ic
it
, a
nd
c
ons
id
e
r
e
d
us
e
le
s
s
)
in
to
in
f
or
m
a
ti
on
or
knowle
dge
or
pa
tt
e
r
ns
f
r
om
la
r
ge
a
m
ount
s
of
da
ta
[
10]
.
D
a
ta
th
a
t
is
c
ons
id
e
r
e
d "
ga
r
ba
ge
"
be
c
a
us
e
i
t
is
not
pa
tt
e
r
ne
d/
not
s
tr
uc
tu
r
e
d
a
nd i
s
not
us
e
f
ul
, i
s
p
r
oc
e
s
s
e
d (
f
il
te
r
)
s
o t
ha
t
i
t
f
or
m
s
in
f
o
r
m
a
ti
on
or
knowle
dge
or
ne
w
pa
tt
e
r
ns
th
a
t
a
r
e
us
e
f
ul
[
9]
.
D
a
ta
m
in
in
g
is
a
pr
oc
e
s
s
to
e
xpl
or
e
th
e
e
xc
e
s
s
va
lu
e
of
in
f
or
m
a
ti
on
th
a
t
w
a
s
not
pr
e
vi
ous
ly
known
to
e
xi
s
t
in
a
da
ta
ba
s
e
.
T
he
pa
tt
e
r
n
s
th
a
t
a
r
e
ve
r
y
us
e
f
ul
a
nd
ha
ve
m
or
e
va
lu
e
th
a
n
th
e
d
a
ta
c
ont
a
in
e
d
in
th
e
da
ta
ba
s
e
a
r
e
obt
a
in
e
d
by
r
e
c
ogni
z
in
g
th
e
in
f
or
m
a
ti
on
obt
a
in
e
d
[
11
]
.
F
r
om
th
e
e
xpl
a
na
ti
on
a
bove
it
c
a
n
be
c
onc
lu
de
d
th
a
t
da
ta
m
in
in
g
is
a
s
te
p
of
a
na
ly
z
in
g
th
e
pr
oc
e
s
s
of
knowl
e
dge
di
s
c
ove
r
y
in
th
e
da
ta
ba
s
e
.
D
a
ta
m
in
in
g
is
a
pr
oc
e
s
s
th
a
t
e
m
pl
oys
on
e
or
m
or
e
m
a
c
hi
ne
l
e
a
r
ni
ng t
e
c
hni
que
s
(
m
a
c
hi
ne
l
e
a
r
ni
ng)
t
o a
na
ly
z
e
a
nd e
xt
r
a
c
t
knowle
dge
a
ut
om
a
ti
c
a
ll
y [
12]
.
A
m
e
th
od
ba
s
e
d
on
le
a
r
ni
ng
f
r
om
a
f
e
a
tu
r
e
th
a
t
is
not
not
ic
e
d
is
c
a
ll
e
d
de
e
p
le
a
r
ni
ng
[
13]
.
N
a
ïv
e
b
a
ye
s
a
lg
or
it
hm
is
one
of
th
e
c
la
r
if
ic
a
ti
on
a
lg
or
it
hm
s
ba
s
e
d
on
th
e
b
a
ye
s
ia
n
th
e
or
e
m
in
s
ta
ti
s
ti
c
s
.
T
h
e
a
va
il
a
bi
li
ty
of
a
c
la
s
s
c
a
n
be
pr
e
di
c
te
d
by
th
e
na
ïv
e
ba
y
e
s
a
l
gor
it
hm
[
14]
.
T
he
na
ïv
e
ba
ye
s
m
e
th
od
is
th
e
be
gi
nni
ng
to
bui
ld
a
m
e
th
od
th
a
t
ha
s
be
e
n
de
s
ig
ne
d
u
s
in
g
a
c
or
pus
th
a
t
ha
s
be
e
n
f
or
m
e
d
[
15]
.
N
a
iv
e
b
a
ye
s
i
s
of
te
n c
a
ll
e
d t
he
b
a
ye
s
'
r
ul
e
w
hi
c
h i
s
a
pr
e
f
ix
f
or
da
ta
m
in
in
g
m
e
th
ods
a
nd ma
c
hi
ne
l
e
a
r
ni
ng. I
t
bui
ld
s
a
m
ode
l
w
it
h
pr
e
di
c
ti
ons
.
T
hi
s
i
s
a
ne
w
w
a
y
to
f
in
d
out
da
ta
a
nd
le
a
r
n m
or
e
[
16]
.
M
ode
li
ng
da
ta
obt
a
in
e
d
by
w
or
ki
ng
w
it
h
bi
na
r
y
da
ta
,
a
nd
is
a
c
a
te
gor
y
of
da
ta
c
a
ll
e
d
r
a
ndom
f
or
e
s
t
[
17]
.
C
la
r
if
ic
a
ti
on
te
c
hni
que
s
on
na
ïv
e
ba
ye
s
c
a
n be
us
e
d a
t
ve
r
y l
a
r
ge
i
npu
t
di
m
e
ns
io
ns
. T
hi
s
i
s
a
s
im
pl
e
a
lg
or
it
hm
but
c
a
n pr
oduc
e
ve
r
y
good
r
e
s
ul
ts
t
ha
n
ot
he
r
a
lg
or
it
hm
s
[
18]
.
2.
R
E
S
E
A
R
C
H
M
E
T
H
O
D
T
he
f
ie
ld
of
s
tu
dy
th
a
t
f
oc
us
e
s
on
a
m
e
th
odol
ogy
to
a
dd
kno
w
le
dge
th
a
t
is
ve
r
y
m
uc
h
us
e
f
ul
f
r
om
th
e
da
ta
is
c
a
ll
e
d
knowl
e
dge
di
s
c
ove
r
y
in
da
ta
b
a
s
e
(
K
D
D
)
.
T
he
r
a
pi
d
de
v
e
lo
pm
e
nt
of
onl
in
e
da
ta
on
a
n
ongo
in
g
ba
s
is
due
to
th
e
w
id
e
s
pr
e
a
d
us
e
of
th
e
in
te
r
ne
t
a
nd
d
a
ta
ba
s
e
s
ha
s
m
a
de
a
ve
r
y
la
r
ge
ne
e
d
f
r
om
th
e
K
D
D
m
e
th
odol
ogy
[
19]
.
O
bs
ta
c
le
s
to
a
ddi
ng
knowl
e
dge
f
r
om
da
ta
to
da
ta
b
a
s
e
r
e
s
e
a
r
c
h,
m
a
c
hi
ne
le
a
r
ni
ng,
knowing
pa
tt
e
r
ns
,
s
ta
ti
s
ti
c
s
,
a
nd
in
c
r
e
a
s
in
g
pe
r
f
or
m
a
nc
e
to
be
c
om
e
s
m
a
r
t
a
nd
s
ophi
s
ti
c
a
te
d
bus
in
e
s
s
s
ol
ut
io
ns
[
20]
.
I
n
th
is
s
tu
dy,
th
e
m
e
th
od
us
e
d
f
or
da
ta
pr
oc
e
s
s
in
g
is
th
e
a
dm
is
s
io
n
da
ta
by
us
in
g
th
e
s
ta
ge
s
of
knowle
dge
di
s
c
ov
e
r
y
in
da
ta
ba
s
e
(
K
D
D
)
a
s
il
lu
s
tr
a
te
d
in
F
ig
ur
e
1
.
K
now
le
dge
di
s
c
ov
e
r
y
in
da
ta
ba
s
e
(
K
D
D
)
is
th
e
pr
oc
e
s
s
of
de
t
e
r
m
in
in
g
us
e
f
ul
in
f
or
m
a
ti
on
a
nd
pa
tt
e
r
ns
i
n
da
ta
.
T
hi
s
i
nf
or
m
a
t
io
n
i
s
c
on
ta
in
e
d
in
a
la
r
ge
da
t
a
b
a
s
e
t
ha
t
w
a
s
pr
e
vi
ou
s
l
y
un
kno
w
n
a
nd
p
ot
e
nt
i
a
l
ly
u
s
e
f
ul
.
D
a
t
a
m
in
i
ng
i
s
on
e
s
t
e
p
i
n
a
s
e
r
i
e
s
of
K
D
D
it
e
r
a
t
iv
e
pr
oc
e
s
s
e
s
[
21]
.
2.1.
D
at
a s
e
le
c
t
io
n
I
n
th
is
pr
oc
e
s
s
th
e
s
e
l
e
c
ti
on
of
da
ta
s
e
ts
i
s
done
, c
r
e
a
ti
ng
a
ta
r
g
e
t
da
ta
s
e
t,
or
f
oc
us
in
g
on
a
s
ubs
e
t
of
va
r
ia
bl
e
s
(
da
ta
s
a
m
pl
e
s
)
w
he
r
e
t
he
di
s
c
ove
r
y w
il
l
be
pe
r
f
o
r
m
e
d
[
22]
. T
he
r
e
s
ul
ts
of
t
he
s
e
le
c
ti
on a
r
e
s
to
r
e
d i
n
a
s
e
pa
r
a
te
f
il
e
f
r
om
th
e
ope
r
a
ti
ona
l
da
ta
ba
s
e
.
T
he
a
tt
r
ib
ut
e
s
us
e
d
a
r
e
s
tu
de
nt
I
D
n
um
be
r
,
na
m
e
,
pr
og
r
a
m
s
tu
dy,
f
a
c
ul
ty
,
ge
nde
r
,
s
c
hool
or
ig
in
,
ye
a
r
of
e
nt
r
y,
na
ti
ona
l
e
xa
m
in
a
ti
on,
ty
pe
of
pa
ym
e
nt
,
a
nd
nom
in
a
l
pa
ym
e
nt
.
T
he
da
ta
in
th
is
s
tu
dy
w
e
r
e
s
our
c
e
d
f
r
om
U
ni
ve
r
s
it
a
s
I
s
la
m
N
e
ge
r
i
R
a
de
n
F
a
t
a
h
w
he
r
e
th
is
da
ta
i
s
s
e
c
onda
r
y
da
ta
c
ons
is
ti
ng
of
ne
w
s
tu
de
nt
da
ta
f
or
2016
up
to
2
019.
T
he
a
m
ount
of
da
ta
obt
a
in
e
d
w
a
s
18,930
c
ons
is
ti
ng
of
s
tu
de
nt
I
D
n
um
be
r
,
na
m
e
,
pr
ogr
a
m
s
tu
dy,
f
a
c
ul
ty
,
pl
a
c
e
of
bi
r
th
,
ge
nde
r
,
da
te
of
bi
r
th
,
s
c
hool
or
ig
in
,
ye
a
r
of
e
nt
r
y,
na
ti
ona
l
e
xa
m
in
a
ti
on,
ty
pe
of
pa
ym
e
nt
,
a
nd
nom
in
a
l
pa
ym
e
nt
.
T
he
f
ol
lo
w
in
g
a
r
e
s
om
e
e
xa
m
pl
e
s
of
ne
w
s
tu
de
nt
s
da
ta
T
a
bl
e
1.
T
he
s
ta
g
e
s
of
th
e
knowle
dge
di
s
c
ove
r
y
in
da
ta
ba
s
e
(
K
D
D
)
pr
oc
e
s
s
c
ons
is
t
of
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
2
,
J
une
20
2
1
:
324
–
331
326
F
ig
ur
e
1. S
ta
ge
s
i
n K
D
D
T
a
bl
e
1
. N
e
w
s
tu
de
nt
da
ta
obt
a
in
e
d
No
S
t
ude
nt
I
D
N
um
be
r
N
a
m
e
S
t
udy P
r
ogr
a
m
G
e
nde
r
Y
e
a
of
E
nt
r
y
S
c
hool
O
r
i
gi
n
N
a
t
i
ona
l
E
xa
m
i
na
t
i
on
T
ype
of
P
a
ym
e
nt
N
om
i
na
l
P
a
ym
e
nt
1
1683600004
S
i
s
ka
A
pr
i
ya
nt
i
H
a
di
t
h S
c
i
e
nc
e
F
2016
I
s
l
a
m
i
c
s
c
hool
N
ur
ul
H
i
km
a
h
77
G
r
oup 3
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p.
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S
i
f
a
ul
H
a
s
a
na
h
Q
ur
a
ni
c
S
c
i
e
nc
e
s
a
nd I
nt
e
r
pr
e
t
a
t
i
on
M
2016
I
s
l
a
m
i
c
s
c
hool
N
ur
ul
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i
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h
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r
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p.
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a
ul
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A
na
m
Q
ur
a
ni
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S
c
i
e
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e
s
a
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e
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pr
e
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a
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on
M
2016
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s
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a
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hool
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B
a
yu
P
ut
r
a
A
qe
e
da
h a
nd
I
s
l
a
m
i
c
P
hi
l
os
ophy
F
2016
S
e
ni
or
H
i
gh
S
c
hool
P
us
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t
a
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oup 3
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2.2. P
r
e
-
p
r
oc
e
s
s
in
g an
d
c
le
an
in
g
D
a
ta
pr
e
-
pr
oc
e
s
s
in
g a
nd da
ta
c
le
a
ni
ng a
r
e
done
by
r
e
m
ovi
ng i
nc
ons
is
te
nt
da
ta
a
nd nois
e
, dupli
c
a
ti
ng
da
ta
, c
or
r
e
c
ti
ng
da
ta
e
r
r
or
s
, a
nd c
a
n be
e
nr
ic
he
d w
it
h r
e
le
va
nt
e
xt
e
r
na
l
da
ta
[
23]
.
2.3. T
r
an
s
f
o
r
m
at
io
n
T
hi
s
pr
oc
e
s
s
tr
a
ns
f
or
m
s
or
c
om
bi
ne
s
da
ta
in
to
a
m
or
e
a
ppr
opr
ia
te
w
a
y
to
do
th
e
m
in
in
g
pr
oc
e
s
s
by
su
m
m
a
r
iz
in
g (
a
ggr
e
ga
ti
on
).
2.4. Dat
a m
in
in
g
A
c
yc
le
t
o
o
b
t
a
i
n
a
p
a
t
t
e
r
n
or
in
f
or
m
a
ti
o
n
t
h
a
t
i
s
v
e
r
y
i
n
t
e
r
e
s
t
e
d
i
n
d
a
t
a
a
n
d
u
s
e
d
b
y
a
te
c
hni
que
[
24]
,
m
e
th
ods
, or
a
lg
or
it
hm
s
unde
r
t
he
obj
e
c
ti
ve
s
of
t
he
K
D
D
pr
oc
e
s
s
i
s
c
a
ll
e
d d
a
ta
m
in
in
g pr
oc
e
s
s
[
10]
.
2.5. I
n
t
e
r
p
r
e
t
at
io
n
/e
val
u
at
io
n
T
he
pr
oc
e
s
s
f
or
tr
a
ns
la
ti
ng
p
a
tt
e
r
ns
ge
ne
r
a
t
e
d
f
r
om
da
ta
m
in
in
g
.
E
va
lu
a
te
(
te
s
t)
w
he
th
e
r
th
e
p
a
tt
e
r
ns
or
in
f
or
m
a
ti
on
f
ound
a
r
e
by
or
c
ont
r
a
di
c
to
r
y
to
pr
e
vi
ous
f
a
c
ts
or
hypothe
s
e
s
.
K
now
le
dge
obt
a
in
e
d
f
r
om
th
e
pa
tt
e
r
ns
f
or
m
e
d i
s
pr
e
s
e
nt
e
d
in
t
he
f
or
m
of
vi
s
u
a
li
z
a
ti
on.
3.
R
E
S
U
L
T
S
A
ND
A
N
A
L
Y
S
I
S
3.1. De
e
p
l
e
ar
n
in
g
T
he
da
ta
pr
oc
e
s
s
in
g
of
ne
w
s
tu
de
nt
s
us
in
g
de
e
p
le
a
r
ni
ng
w
it
h
r
a
pi
dm
in
e
r
s
of
twa
r
e
is
s
how
n
i
n
F
ig
ur
e
2.
D
e
ta
il
of
th
e
va
li
da
ti
on
pr
oc
e
s
s
in
de
e
p
le
a
r
ni
ng
a
s
s
how
n
in
F
ig
ur
e
3.
U
s
in
g
de
e
p
le
a
r
ni
ng
m
ode
li
ng
a
s
s
how
n
in
F
ig
ur
e
2
,
w
it
h
th
e
a
m
ount
of
tr
a
in
in
g
d
a
ta
(
ne
w
s
tu
de
nt
a
dm
is
s
io
n
da
ta
f
r
om
2016
to
2019)
r
e
c
e
iv
in
g
18.930
it
e
m
s
.
T
he
a
c
c
ur
a
c
y
of
u
s
in
g
de
e
p
l
e
a
r
ni
ng
is
52.65%
a
s
s
how
n
in
F
ig
ur
e
4.
B
e
s
id
e
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
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2252
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8938
A
c
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n de
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p l
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a
r
ni
ng, naïv
e
bay
e
s
and r
andom
fo
r
e
s
t
fo
r
t
he
appli
c
at
io
n of
…
(
N
u
r
hac
hi
ta
)
327
pr
oduc
in
g
a
n
a
c
c
ur
a
c
y
va
lu
e
,
de
e
p
le
a
r
ni
ng
a
ls
o
pr
oduc
e
s
a
ka
ppa
va
lu
e
of
0.511,
a
c
or
r
e
la
ti
on
va
lu
e
of
0.804, a
nd a
c
r
os
s
-
e
nt
r
opy va
lu
e
of
1.793 a
s
s
how
n i
n F
ig
ur
e
5.
F
ig
ur
e
2. D
e
e
p l
e
a
r
ni
ng mode
li
ng on
r
a
pi
dm
in
e
r
F
ig
ur
e
3
. V
a
li
da
ti
on de
e
p l
e
a
r
ni
ng
F
ig
ur
e
4
. V
ie
w
a
c
c
ur
a
c
y
de
e
p l
e
a
r
ni
ng
3.2. Naïve
b
aye
s
T
he
da
ta
pr
oc
e
s
s
in
g
of
ne
w
s
tu
de
nt
s
us
in
g
na
ïv
e
ba
ye
s
w
it
h
r
a
pi
dm
in
e
r
s
of
twa
r
e
is
s
how
n
in
F
ig
ur
e
6
.
U
s
in
g
na
ïv
e
ba
ye
s
m
ode
li
ng
a
s
s
how
n
in
F
ig
ur
e
6
,
w
it
h
th
e
a
m
ount
of
tr
a
in
in
g
da
ta
(
ne
w
s
tu
de
nt
a
dm
is
s
io
n
da
ta
f
r
om
2016
to
2019)
r
e
c
e
iv
in
g
18.930
it
e
m
s
a
nd
te
s
ti
ng
da
ta
us
in
g
2019
n
e
w
s
t
u
d
e
n
t
a
d
m
i
s
s
i
o
n
d
a
t
a
w
i
t
h
a
t
o
t
a
l
o
f
4
7
6
2
i
t
e
m
s
.
T
h
e
a
c
c
ur
a
c
y
of
u
s
i
n
g
n
a
ï
v
e
ba
y
e
s
i
s
9
9
.
7
9%
a
s
s
h
o
w
n
i
n
F
ig
u
r
e
7.
B
e
s
id
e
s
pr
oduc
in
g
a
n
a
c
c
ur
a
c
y
va
lu
e
,
na
ïv
e
b
a
ye
s
a
l
s
o
pr
oduc
e
s
a
k
a
pp
a
va
lu
e
of
0.998,
a
c
or
r
e
la
ti
on
va
lu
e
of
0.998,
a
nd a
c
r
os
s
-
e
nt
r
opy va
lu
e
of
0.029 a
s
s
how
n i
n F
ig
ur
e
8.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
2
,
J
une
20
2
1
:
324
–
331
328
3.3. Ran
d
om
f
or
e
s
t
T
he
da
ta
pr
oc
e
s
s
in
g
of
ne
w
s
tu
de
nt
s
us
in
g
r
a
ndom
f
or
e
s
t
w
i
th
r
a
pi
dm
in
e
r
s
of
twa
r
e
is
s
how
n
in
F
ig
ur
e
9.
U
s
in
g
r
a
ndom
f
or
e
s
t
m
ode
li
ng
a
s
s
how
n
in
F
ig
ur
e
9
,
w
it
h
th
e
a
m
ount
of
tr
a
in
in
g
da
ta
(
ne
w
s
tu
de
nt
a
dm
is
s
io
n
da
ta
f
r
om
2016
to
2019
r
e
c
e
iv
in
g
18,930
a
nd
te
s
ti
n
g
da
ta
us
in
g
2019
ne
w
s
tu
d
e
nt
a
dm
is
s
io
n
da
t
a
w
it
h a
t
ot
a
l
of
4762.
T
he
a
c
c
ur
a
c
y of
u
s
in
g r
a
ndom f
or
e
s
t
44.65%
a
s
s
how
n i
n F
ig
ur
e
10.
F
ig
ur
e
5
. V
ie
w
ka
ppa
de
e
p l
e
a
r
ni
ng
F
ig
ur
e
6. N
a
ïv
e
ba
ye
s
m
ode
li
ng on r
a
pi
dm
in
e
r
F
ig
ur
e
7
. V
ie
w
a
c
c
ur
a
c
y
na
ïv
e
ba
y
e
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
c
om
par
is
on be
tw
e
e
n de
e
p l
e
a
r
ni
ng, naïv
e
bay
e
s
and r
andom
fo
r
e
s
t
fo
r
t
he
appli
c
at
io
n of
…
(
N
u
r
hac
hi
ta
)
329
F
ig
ur
e
8
. V
ie
w
ka
ppa
na
ïv
e
ba
ye
s
F
ig
ur
e
9
.
R
a
ndom f
or
e
s
t
m
ode
li
ng on r
a
pi
dm
in
e
r
F
ig
ur
e
10
. V
ie
w
a
c
c
ur
a
c
y
r
a
ndom f
or
e
s
t
B
e
s
id
e
s
pr
oduc
in
g
a
n
a
c
c
ur
a
c
y
v
a
lu
e
,
r
a
ndom
f
or
e
s
t
a
ls
o
p
r
oduc
e
s
a
ka
pp
a
va
lu
e
of
0.421,
a
c
or
r
e
la
ti
on
va
lu
e
of
0.729,
a
nd
a
c
r
o
s
s
-
e
nt
r
opy
va
lu
e
of
2.003
,
a
s
s
ho
w
n
in
F
ig
ur
e
11.
T
he
pa
r
a
m
e
te
r
r
e
s
ul
ts
of
a
c
c
ur
a
c
y,
pr
e
c
is
io
n,
r
e
c
a
ll
c
a
n
be
s
a
id
to
pr
oduc
e
go
o
d
c
la
s
s
if
ic
a
ti
on
r
e
s
ul
ts
or
not
by
us
in
g
th
e
c
la
s
s
if
ic
a
ti
on
r
e
s
ul
t
p
a
r
a
m
e
te
r
gui
de
li
ne
s
s
how
n
in
T
a
bl
e
2
[
2
5]
.
T
he
r
e
s
ul
ts
of
th
e
c
om
pa
r
is
on
be
tw
e
e
n
th
e
th
r
e
e
a
lg
or
it
hm
s
na
m
e
ly
de
e
p l
e
a
r
ni
ng, na
ïv
e
ba
y
e
s
, a
nd r
a
ndo
m
f
or
e
s
t,
a
s
s
how
n i
n T
a
bl
e
2.
T
he
hi
ghe
s
t
a
c
c
ur
a
c
y
va
lu
e
is
n
a
ïv
e
ba
y
e
s
w
it
h
a
va
lu
e
of
99.7
9%
,
w
hi
le
f
or
th
e
hi
ghe
s
t
ka
pp
a
va
lu
e
is
na
ïv
e
ba
ye
s
w
it
h
a
va
lu
e
of
0.998,
w
hi
le
f
or
th
e
hi
ghe
s
t
c
o
r
r
e
la
ti
on
va
lu
e
is
n
a
ïv
e
b
a
ye
s
w
it
h
a
va
lu
e
of
0.998, a
nd f
or
t
he
hi
ghe
s
t
c
r
os
s
-
e
nt
r
opy va
lu
e
t
ha
t
is
r
a
ndom f
o
r
e
s
t
w
it
h a
va
lu
e
of
2.003.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
10
, N
o.
2
,
J
une
20
2
1
:
324
–
331
330
F
ig
ur
e
11
. V
ie
w
a
c
c
ur
a
c
y
r
a
ndom f
or
e
s
t
T
a
bl
e
2. R
e
s
ul
ts
c
om
p
a
r
is
on of
a
ll
t
hr
e
e
a
lg
or
it
hm
s
R
e
s
ul
t
T
he
a
l
gor
i
t
hm
D
e
e
p l
e
a
r
ni
ng
N
a
ï
ve
ba
ye
s
R
a
ndom
f
or
e
s
t
A
c
c
ur
a
c
y
52.65%
99.79%
44.65%
K
a
ppa
0.511
0.998
0.421
C
or
r
e
l
a
t
i
on
0.804
0.998
0.729
C
r
os
s
-
e
nt
r
opy
1.793
0.029
2.003
4.
C
O
N
C
L
U
S
I
O
N
B
a
s
e
d
on
th
e
r
e
s
e
a
r
c
h
a
nd
di
s
c
us
s
io
n
th
a
t
ha
s
be
e
n
c
a
r
r
ie
d
o
ut
,
it
c
a
n
be
c
onc
lu
de
d
th
a
t
f
r
om
th
e
th
r
e
e
m
e
th
ods
of
de
e
p
le
a
r
ni
ng,
na
ïv
e
ba
ye
s
,
a
nd
r
a
ndom
f
or
e
s
t
in
de
te
r
m
in
in
g
th
e
be
s
t
s
tu
de
nt
r
e
c
r
ui
tm
e
nt
pr
om
ot
io
n
s
tr
a
te
gy
a
t
th
e
R
a
d
e
n
F
a
ta
h
S
ta
te
I
s
la
m
ic
U
ni
ve
r
s
i
ty
in
P
a
le
m
ba
ng
a
nd
r
e
f
e
r
r
in
g
to
th
e
or
ig
in
a
l
da
ta
.
D
a
ta
of
ne
w
s
tu
de
nt
s
us
e
d
f
r
om
2016
to
2019
w
e
r
e
18.930
it
e
m
s
a
s
da
ta
tr
a
in
in
g
a
nd
da
ta
te
s
ti
ng
us
e
d
da
ta
of
ne
w
s
tu
de
nt
s
f
r
om
2019
w
e
r
e
4762
it
e
m
s
. T
he
r
e
s
ul
t
s
o
f
th
is
s
tu
dy
u
s
e
d
de
e
p
le
a
r
ni
ng
r
e
s
ul
ts
r
e
s
ul
te
d
in
a
n
a
c
c
ur
a
c
y
va
lu
e
of
52.65%
,
na
ïv
e
ba
ye
s
r
e
s
ul
ts
r
e
s
ul
te
d
in
a
n
a
c
c
ur
a
c
y
va
lu
e
of
99.79%
,
a
nd
r
a
ndom
f
or
e
s
t
r
e
s
ul
ts
r
e
s
ul
te
d
in
a
n
a
c
c
ur
a
c
y
va
lu
e
of
44.65%
.
S
o
of
th
e
th
r
e
e
a
lg
or
it
hm
s
th
a
t
s
how
th
e
be
s
t
r
e
s
ul
ts
f
or
th
e
pr
om
ot
io
n
s
tr
a
te
gy
of
in
c
om
in
g
ne
w
s
tu
d
e
nt
s
th
a
t
a
r
e
us
in
g
na
iv
e
b
a
ye
s
w
it
h
th
e
hi
ghe
s
t
a
c
c
ur
a
c
y
va
lu
e
of
99.79%
.
R
E
F
E
R
E
N
C
E
S
[1]
U.
Fayyad,
G.
Piatetsky
-
Shapiro
,
and
P.
Smyth,
“From
data
minin
g
to
knowle
dge
discove
ry
in
databa
ses,”
AI
Magazine
, vol. 17, no. 3, pp. 37
-
53, 1996.
[2]
Nurhachita
and
E.
S.
Negara,
“A
Comparison
Between
Naïve
Bayes
and
the
K
-
Means
Clustering
Algorithm
fo
r
The Application of Data Mi
ning on The Admission of New Students,”
Jurnal
Intelektu
alita:
Keisla
man, Sos
ial, d
an
Sains
, vol. 9, no. 1, pp. 51
-
62, 2020
,
https://doi.org/10.19109/intelektualita.v9i1.5574
.
[3]
D.
F.
Brianna,
E.
Surya
Negara,
and
Y.
N.
Kunang,
“Network
Centr
alization
Analys
is
Approach
in
the
Spread
of
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ce
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J
A
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ti
f
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e
ll
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S
S
N
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A
c
om
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is
on be
tw
e
e
n de
e
p l
e
a
r
ni
ng, naïv
e
bay
e
s
and r
andom
fo
r
e
s
t
fo
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t
he
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c
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u
r
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“Drying
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sabdariffa
)
Fl
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Pe
tals
using
Sola
r
Dryer
wi
th
Doub
le
Glass
C
ove
r
C
o
l
l
e
c
to
r
,
”
In
t
e
r
na
t
i
o
na
l
J
ou
r
n
a
l
o
f
S
ci
e
n
c
e
a
n
d
En
g
i
n
e
er
i
n
g
,
v
o
l
.
7
,
n
o
.
2,
p
p
.
1
5
5
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16
0
,
2
01
4
,
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on
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e on Big
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t
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ne
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i
a
n
re
s
e
a
r
ch
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a
r
Ea
s
t
J
o
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rn
a
l
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f
E
le
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t
r
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ni
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m
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n
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,
”
I
n
t
el
l
i
g
en
t
Sys
t
e
m
s
Re
f
e
r
en
c
e
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i
b
r
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B
I
O
G
R
A
P
H
I
E
S
O
F
A
U
T
H
O
R
S
Nurhachita
has
obtained
her
bachelor’s
of
Information
System
from
Universitas
Islam
Negeri
Raden
Fatah
Palembang.
She
is
a
master
student
majoring
in
I
nformatics
Engineering
at
Universitas
Bina
Darma.
And
then,
she
works
at
Universitas
Is
lam
Negeri
Raden
Fatah
Palemb
ang.
Edi
Surya
Negara
has
obtained
his
bachelor’s
and
master
of
informa
tics
from
Universitas
Bina
Darma
and
Doctor
of
Information
Technology
from
Gunadarma
Uni
versity.
He
has
10
years
of
teaching a
nd resea
rch expe
rience.
He publishe
s 9 resea
rch pape
rs at the in
ternationa
l level.
Evaluation Warning : The document was created with Spire.PDF for Python.