Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
5, N
o
. 1
,
Febr
u
a
r
y
201
5,
pp
. 12
9
~
13
5
I
S
SN
: 208
8-8
7
0
8
1
29
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Decision Support System for the
Selection of Courses in the
High
er Educati
o
n using t
h
e M
e
th
od of Eli
m
ination
Et Ch
oi
x
Tranduit La Realite
M
a
d
e
Su
da
rma
1
, An
ak Agu
n
g Kompiang Oka
Su
dana
2
,
Irwans
yah Cahya
3
1,3
Departem
ent
o
f
El
ectr
i
c
a
l
Engi
neering
,
Com
p
uter System
and Inform
atics,
Uday
ana University
, Indonesia
2
Departement of
Information
Technol
og
y
,
Uday
a
n
a University
, In
donesia
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Oct 5, 2014
R
e
vi
sed Dec 9,
2
0
1
4
Accepte
d Ja
n
5, 2015
Each
y
e
ar thous
ands of prospective st
udents attend new student enrollment in
universities, which each pros
pective student h
a
ve
dete
rm
ined th
e c
ourses that
wish to be studied in college. Most
of prospective student choose the courses
only
based on the number of en
thusiasts
and wishes of parents, and are not
based on their
academic ab
ility
.
The imp
act of
th
is phenomenon is that man
y
of the prospective students
chosen to switch cou
r
ses and not
a f
e
w of them
have been punished dropout. This problem can be solved through the
creation of decision support sy
s
t
em that
has an
ability
to suggest suitable
cours
e
s
to be s
e
lec
t
ed b
y
th
e pr
os
pectiv
e s
t
uden
t
bas
e
d on th
eir
acad
em
ic
ability
.
This decision support s
y
stem so
lved the pr
oblem using the
method of
elimination et choix trandu
it la real
ite which is presented in
web-based
application to
raise the accessib
ility
b
y
the prospectiv
e studen
t
.
Keyword:
Co
urses
Deci
si
o
n
S
u
pp
ort
Sy
st
em
ELECTRE Me
thod
Web-b
a
sed App
licatio
n
Copyright ©
201
5 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
M
a
de S
u
darm
a
Depa
rt
em
ent
of El
ect
ri
cal
an
d
C
o
m
put
er Sy
s
t
em
Engi
nee
r
i
n
g,
Engineeri
n
g Fa
culty,
Ud
ayan
a Un
iv
ersity,
Ji
m
b
ar
an
Cam
p
u
s
,
K
u
ta 803
61
, Bali,
I
ndo
n
e
sia.
Em
a
il:
m
s
u
d
a
rma@u
nud
.ac.i
d
, Telp
./Fax
.
: +6
236
170
331
5
1.
INTRODUCTION
The el
ect
i
on
o
f
co
urses at
t
h
e col
l
e
ge l
e
vel
i
s
t
h
e
m
o
st
im
port
a
nt
st
age
s
fo
r a pr
os
pec
t
i
v
e st
ude
nt
,
wh
ich
all o
f
t
h
e
m
m
u
st d
e
termin
e th
e scientific field
th
at
wante
d
to
be learne
d
or the c
o
urses that correlates
wi
t
h
t
h
e
pr
of
essi
on t
o
be
achi
e
ve
d. E
v
e
r
y
y
ear t
h
o
u
s
a
nd
s o
f
p
r
osp
ect
i
v
e st
ude
nt
at
t
e
nd ne
w
st
ude
n
t
en
ro
llm
en
t in
u
n
i
v
e
rsities,
wh
ich
each
of pro
s
p
ectiv
e
stud
en
t
h
a
v
e
d
e
term
in
ed
th
e cou
r
ses th
at
wish
to b
e
st
udi
e
d
i
n
c
o
l
l
e
ge.
H
o
we
ver
m
o
st
of p
r
os
p
ect
i
v
e st
ud
ent
cho
o
se t
h
e c
o
urses
o
n
l
y
bas
e
d
on t
h
e
num
ber
o
f
en
thu
s
iasts and
wish
es of
paren
t
s, an
d are no
t
b
a
sed on
th
ei
r
o
w
n
acad
em
ic ab
ilit
y. Th
e im
p
act
of th
i
s
phe
n
o
m
e
non
i
s
t
h
at
m
a
ny
of
t
h
e
pr
os
pect
i
v
e st
u
d
ent
s
ch
o
s
en t
o
s
w
i
t
c
h
cou
r
ses
an
d
n
o
t
a fe
w
of
t
h
e
m
have
been
p
uni
s
h
e
d
dr
o
p
o
u
t
.
T
h
i
s
pr
o
b
l
e
m
can be sol
v
e
d
t
h
r
o
u
gh t
h
e creat
i
o
n
of
deci
si
o
n
s
u
pp
o
r
t
sy
st
em
that
has
an
ab
ility to
su
gg
est su
itab
l
e co
urses t
o
be selected
b
y
th
e pro
s
p
ective stu
d
e
n
t
b
a
sed
on
th
ei
r acad
e
m
i
c
ab
ility.
D
ecision
suppo
r
t
system
is an
in
for
m
at
io
n
syste
m
at
th
e man
a
g
e
m
e
n
t
l
e
v
e
l of
an
o
r
gan
i
zatio
n
th
at
com
b
ines data
and s
ophisticated a
n
alytic
al m
odels
t
o
sup
p
o
rt
deci
si
on
-m
aki
ng i
n
co
n
d
i
t
i
on
o
f
sem
i
-
str
u
ctur
ed
and
u
n
s
t
r
u
c
t
u
r
e
d
.
D
ecision
suppo
r
t
system
can
b
e
in
terp
r
e
ted asa m
o
d
e
l-
b
a
sed
system
co
n
s
istin
g
of
pr
oce
d
u
r
es
i
n
p
r
oces
si
n
g
t
h
e dat
a
a
nd t
h
e res
u
l
t
s
o
f
t
h
e dat
a
pr
oces
si
ng i
s
use
d
t
o
assi
st
m
a
nagers i
n
m
a
ki
ng
deci
si
ons
. T
h
i
s
m
odel
-
base
d sy
st
em
shoul
d be s
i
m
p
l
e
, ro
bust
,
easi
l
y
cont
rol
l
ed, a
d
apt
a
bl
e,
easi
l
y
co
mm
u
n
i
cated
and
im
p
lic
itly
also
m
ean
s the syste
m
m
u
st
b
e
b
a
sed
co
m
p
u
t
er so
th
at syste
m
can
fu
lfi
ll it
s
p
u
rp
o
s
e [8
]-
[11
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE Vo
l. 5
,
N
o
. 1
,
Febru
a
ry
2
015
:
12
9
–
13
5
13
0
The
deci
si
on
s
u
p
p
o
rt
sy
st
em
sol
v
e
d
t
h
e c
o
u
r
ses el
ect
i
on
p
r
o
b
l
e
m
usi
ng t
h
e M
e
t
h
od
of
El
im
i
n
at
i
on
Et Ch
o
i
x Tra
n
d
u
it La
Rea
lite
or
known a
s
Method of EL
ECTRE. Th
e
basic conce
p
t of ELECTRE m
e
thod is
t
o
ha
n
d
l
e
t
h
e
o
u
t
r
a
nki
ng
rel
a
t
i
ons
hi
p
usi
n
g
pai
r
wi
se
co
m
p
arison
s
b
e
tween
th
e on
e altern
ativ
e
with
t
h
e o
t
h
e
r
alternatives
on each c
r
iterion se
parately [1],
[2],
[10]. T
h
e
Outra
n
king
re
lations
of
an
d
ex
pl
ai
ne
d t
h
a
t
whe
n
th
e-
i
th
altern
ativ
e
d
i
dn’t do
m
i
n
a
te th
e-
j
th
altern
ative q
u
an
titativ
ely, th
en
t
h
e decisio
n
m
a
k
e
r still can
tak
e
th
e
r
i
sk by cho
o
sing
be
cause
is alm
o
st b
e
tter th
an
. Th
e altern
ative is sai
d
t
o
b
e
d
o
m
in
ated
if
th
ere is ano
t
h
e
r altern
ativ
e t
h
at o
u
t
p
e
rform
t
h
em
in
o
n
e
or
m
o
re o
f
th
e same attrib
u
t
es an
d
i
n
th
e rem
a
in
in
g
attrib
u
t
es.
The
Decision
maker is aske
d to assi
g
n
pre
f
e
r
ence wei
g
ht
s or
i
m
port
a
nt
fa
ct
or of
c
r
i
t
e
ri
a t
o
re
veal
t
h
e
relative im
port
a
nce of these
criteria
[4].
A series of asse
ssm
ent proces
s carried
ou
t in
a ro
w ag
ainst th
e
out
ran
k
i
n
g rel
a
t
i
ons o
f
al
t
e
rnat
i
v
es. C
o
nc
or
da
nce i
s
def
i
ned as t
h
e se
t
of som
e
evi
d
ence t
o
s
u
pp
ort
t
h
e
concl
u
si
o
n
t
h
at
outperform
or
dom
inate
. T
h
e set
o
f
Di
sc
o
r
da
nce
i
s
defi
n
e
d as
t
h
e am
ou
nt
of
evi
d
e
n
ce
to
supp
ort th
e co
n
c
l
u
sion
that
is wo
rse th
an
[
5
]
,
[
7
]
.
Thi
s
m
e
t
hod
has a cl
eare
r
vi
ew a
b
o
u
t
t
h
e
altern
ativ
e is t
o
elim
in
ate alt
e
rn
ativ
es th
at
are less
fa
v
o
rab
l
e,
wh
en faci
n
g
m
u
ltip
le criteria with
a
n
u
m
b
er o
f
alternatives i
n
the case
of de
c
i
sion m
a
king [3].
2.
R
E
SEARC
H M
ETHOD
Th
is
d
ecision
su
ppo
rt system
is d
e
lib
erately d
e
sign
ed
to b
e
ab
le to prov
id
e a so
lu
tion
i
n
determin
in
g
t
h
e choi
ce
of c
o
u
r
ses i
n
Hi
g
h
e
r Ed
ucat
i
o
n.
Thi
s
ap
pl
i
cat
i
on desi
gne
d usi
ng P
H
P
pr
og
ra
m
m
i
ng l
a
ngua
ge an
d
HTM
L
,
w
h
i
c
h
i
s
i
n
t
e
grat
e
d
w
i
t
h
seve
ral
ot
he
r
pr
og
ram
m
i
ng
l
a
ng
ua
ges s
u
c
h
as
Java
Scri
pt
, Jq
ue
ry
an
d C
SS.
2.
1.
Sys
t
em
Co
nce
p
t
The
use o
f
t
h
i
s
deci
si
o
n
s
u
p
p
o
r
t
sy
st
em
for t
h
e sel
ect
i
o
n
of c
o
urses
be
g
i
ns wi
t
h
t
h
e l
o
gi
n
pr
ocess
.
Pr
osp
ectiv
e stud
en
t
who
su
ccessf
u
lly
p
e
rf
orm
th
e lo
g
i
n pr
ocess can
start the decision
mak
i
ng
o
f
th
e selectio
n
of
courses, by providing
input data in the form
of academ
i
c
ability
and econom
ic abilit
y of t
h
e prospective
stu
d
e
n
t
itself. Th
e acad
e
m
i
c
ab
ility is co
mp
rised
of th
e
v
a
lu
e
o
f
stud
en
t report cards fro
m
g
r
ad
e 1 in
1
s
t
sem
e
st
er t
o
gra
d
e
3 i
n
2
n
d
se
m
e
st
er, w
h
en
t
h
e
pr
os
pect
i
v
e
st
ude
nt
were at
hi
g
h
sch
o
o
l
l
e
vel
[
6
]
.
Th
e i
n
pu
t
d
a
ta
is con
v
e
rted into
a
weigh
t
v
a
l
u
e i
n
acco
r
d
a
nce with th
e syste
m
p
r
ov
ision
s
and
pu
t in
th
e in
pu
t d
a
tabase, co
m
p
lete
with
id_
u
s
er belo
ng
s users
wh
o
h
a
v
e
g
i
v
e
n
th
e in
pu
t d
a
ta.
Th
e wei
g
h
t
v
a
lu
e of
in
pu
t d
a
ta that alread
y exist in
th
e inp
u
t
d
a
tab
a
se
p
a
ssed
to
t
h
e p
r
o
cess
of
v
a
riab
le in
itializatio
n
si
m
u
ltan
e
o
u
s
ly
with
th
e
d
a
ta
o
f
altern
ativ
e
weigh
t
tak
e
n f
r
om
t
h
e cou
r
s
e
s dat
a
base
. A
l
l
dat
a
t
h
at
has
been
initialized is forwarde
d to the cal
culation
proces
s of decis
i
on-m
aki
ng usi
ng t
h
e ELECT
R
E
m
e
thod
[1]
.
The
resul
t
of t
h
e ca
l
c
ul
at
i
on
pr
oce
ss o
f
deci
si
on
-
m
aki
ng i
s
a
su
gge
st
i
on i
n
t
h
e
fo
rm
of co
u
r
s
e
st
hat
sui
t
a
bl
e
t
o
b
e
selected
b
y
th
e u
s
er, wh
ich
h
a
s b
een
sorted
by th
e syste
m
b
a
sed
on
th
e acq
u
i
sition
of th
e d
o
m
in
an
ce v
a
lu
e of
each c
o
urses
[9].
2.
2.
Rese
arch
P
h
a
s
es
Thi
s
researc
h
was c
o
nd
uct
e
d
t
h
r
o
ug
h se
ve
r
a
l
st
ages, a
s
f
o
l
l
ows:
1.
Determ
in
atio
n
o
f
prob
lem
s
o
r
cases th
at ex
amin
ed
in
t
h
is st
u
d
y
and
lim
i
t
at
io
n
s
of th
e prob
lem
i
t
self.
2.
Th
e co
llectio
n of
d
a
ta wh
ich
is
related
t
o
th
e is
s
u
es. T
h
e
data c
o
llection
was
done
by m
eans of a
literatu
re stud
y
.
3.
Designing the
syste
m
in accorda
n
ce
with the problem
s
studie
d
and t
h
e data
obtained, as
well as
im
ple
m
ent the ELECTRE m
e
thod t
o
the
syste
m
are m
a
de.
4.
Co
nn
ecting
th
e in
terface
o
f
sy
ste
m
with
a
d
a
tab
a
se t
h
at h
a
s
b
een created
.
5.
Co
ndu
ct testing
to th
e
syste
m
th
at
ha
s
been
designe
d
a
n
d c
r
eated.
6.
Perform
i
n
g
an
an
alysis on
th
e resu
lts of th
e testin
g
system
.
7.
M
a
ki
n
g
c
oncl
u
si
ons
.
8.
Pre
p
arat
i
o
n
of
rep
o
rt
s
base
d
o
n
t
h
e
st
ages
o
f
t
h
e resea
r
c
h
t
h
at
has
bee
n
do
ne.
3.
R
E
SU
LTS AN
D ANA
LY
SIS
The
pu
rp
ose
o
f
t
e
st
s pe
rf
or
m
e
d on
ap
pl
i
cat
i
ons
of
deci
si
on s
u
pp
o
r
t
s
y
st
em
for t
h
e
sel
ect
i
on o
f
co
urses is to determin
e th
e effectiv
en
ess
and
p
e
rform
a
n
ce o
f
app
licatio
n
th
at hav
e
b
e
en
created
. Th
e test will
pr
o
v
i
d
e a conc
l
u
si
o
n
on
ho
w
effect
i
v
e t
h
e m
e
t
hod co
ul
d
sol
v
e t
h
e p
r
o
b
l
e
m
s
and ho
w wel
l
t
h
e perf
or
m
a
nce
im
pl
em
ent
e
d.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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208
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7
0
8
D
ecision
S
uppo
rt
S
y
stem
f
o
r t
h
e
S
e
lectio
n o
f
Co
u
rses in th
e
H
i
gh
er Edu
c
a
t
io
n
u
s
i
n
g th
e
…
(Ma
d
e
Su
darm
a
)
13
1
3.1.
Test the
Accuracy of
the Calcula
t
ion Results
Testing the ac
curacy of the
calculation ca
n be done
t
h
rough t
h
e com
p
letion
of a case,
whic
h is as
fo
llows.
The p
r
o
s
pect
i
v
e st
ude
nt
nam
e
l
y
“user” m
a
ki
n
g
a sel
ect
i
on o
f
co
urses
u
s
i
ng t
h
i
s
deci
s
i
on s
u
p
p
o
rt
sy
st
em
appl
i
cat
i
on,
w
h
ere
t
h
e
use
r
i
n
p
u
t
(
v
al
ue
of
re
po
rt
car
ds)
are
as
fol
l
o
ws.
1.
Ave
r
a
g
e
val
u
e
of
I
n
d
o
n
esi
a
L
a
ng
ua
ge:
8
0
2.
Ave
r
a
g
e val
u
e of
En
gl
i
s
h:
9
0
3.
Av
erag
e v
a
lu
e o
f
m
a
th
e
m
a
tics
:
7
0
4.
Av
erag
e v
a
lu
e o
f
In
don
esian Literatu
re: 9
0
5.
Ave
r
a
g
e val
u
e of
Fo
rei
g
n Lan
gua
ge:
70
6.
Av
erag
e v
a
lu
e o
f
An
thropo
log
y
:
70
7.
Ave
r
a
g
e value of
Com
puter Science:
75
8.
Econ
o
m
ic cap
acity p
e
r
1
sem
e
ster
: ID
R 3.000
.0
00
,00
Com
p
letion of
the above case
s
usi
n
g m
a
nual cal
culation of
ELECTRE m
e
th
od is
as
follows.
Altern
ativ
e m
a
trix
(Matri
x
X
) is:
Tab
l
e 1
.
Alternativ
e
Matrix
Alternative (Cours
e
s)
W
e
ight value of each cr
iter
i
on
1
2 3 4 5 6 7 8
I
ndonesian
L
iter
a
tur
e
5
2 2 5 3 2 3 1
Ancient
Javanese
L
iter
a
ture
4
2 2 3 3 3 2 1
L
iter
a
ture
of
Bali
4
2 2 4 3 4 3 1
E
nglish
L
iter
a
tur
e
3
5 2 2 4 2 3 2
Japanese
L
iter
a
tu
re
3
3 2 2 5 3 2 2
Ar
cheology
2
3 4 2 3 5 4 1
Cultur
a
l
Anthr
opol
ogy
2
3 2 2 4 5 3 1
Histor
y
3
3 2 2 2 4 3 1
Whe
r
e t
h
e representation of t
h
e
weight val
u
e is:
5
=
v
e
r
y
go
od
valu
e
4= go
o
d
val
u
e
3
=
en
oug
h v
a
l
u
e
2= bad
val
u
e
1= very
ba
d va
l
u
e
The i
n
p
u
t
dat
a
fr
om
user
(
v
a
l
ue o
f
re
po
rt
c
a
rds
)
c
o
nve
rt
e
d
i
n
t
o
a
p
r
efe
r
ence
wei
g
ht
b
a
sed
o
n
t
h
e
fo
llowing
con
d
itio
n
s
.
If t
h
e
v
a
lu
e is in
th
e rang
e
8
5
to
100
, t
h
en the weigh
t
o
f
preferen
ce is
5
.
If t
h
e
v
a
lu
e is in
th
e rang
e
8
0
to
84
, th
en
the
weigh
t
of
p
r
eferen
ce is 4.
If t
h
e
v
a
lu
e is in
th
e rang
e
7
5
to
79
, th
en
the
weigh
t
of
p
r
eferen
ce is 3.
If t
h
e
v
a
lu
e is in
th
e rang
e
6
5
to
74
, th
en
the
weigh
t
of
p
r
eferen
ce is 2.
If t
h
e
v
a
lu
e is in
th
e rang
e
1
0
to
64
, th
en
the
weigh
t
of
p
r
eferen
ce is 1.
Th
e econo
m
i
c
ab
ility p
e
r 1
semester o
f
u
s
er
is also
co
nv
erted
in
to
a
p
r
eferen
ce
weigh
t
based
on
th
e
fo
llowing
con
d
itio
n
s
.
If t
h
e
val
u
e i
s
i
n
t
h
e
ra
n
g
e R
p
.
4
.
2
0
0
.
0
00
,0
0 t
o
R
p
.
2
0
.
0
0
0
.
0
00
,0
0 t
h
en
t
h
e
wei
g
ht
o
f
pre
f
e
r
ence
i
s
5
.
If t
h
e
v
a
lu
e is in
th
e rang
e Rp.3
.1
00
.00
0
,00
t
o
R
p
.4
.1
00
.000
,0
0 th
en
th
e
weigh
t
of
p
r
eferen
ce is 4.
If t
h
e
v
a
lu
e is in
th
e rang
e Rp.2
.6
00
.00
0
,00
t
o
R
p
.3
.0
00
.000
,0
0 th
en
th
e
weigh
t
of
p
r
eferen
ce is 3.
If t
h
e
v
a
lu
e is in
th
e rang
e Rp.2
.1
00
.00
0
,00
t
o
R
p
.2
.5
00
.000
,0
0 th
en
th
e
weigh
t
of
p
r
eferen
ce is 2.
If t
h
e
v
a
lu
e is in
th
e rang
e Rp.1
.0
00
.00
0
,00
t
o
R
p
.2
.0
00
.000
,0
0 th
en
th
e
weigh
t
of
p
r
eferen
ce is 1.
Table 2. Pre
f
erence weight
I
nput data fr
o
m
user
Pref
erence
weights (interest
rate
of
cri
t
erion)
Criteria
1 2 3 4 5 6 7 8
4 5 2 5 2 2 3 3
Whe
r
e t
h
e representation of
t
h
e pre
f
ere
n
ce weight
is:
5= very
i
m
port
a
nt
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
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08
I
J
ECE Vo
l. 5
,
N
o
. 1
,
Febru
a
ry
2
015
:
12
9
–
13
5
13
2
4= i
m
port
a
nt
3= qui
t
e
i
m
por
t
a
nt
2= not
i
m
port
a
nt
1= very
u
n
i
m
p
o
rt
a
n
t
Phase
1
.
Determin
atio
n
of t
h
e no
rm
alized
matrix
.
=
∑
unt
uk
i
=1
,2,
3
,…,
m
dan
j
=1
,2
,3
,
…
,
n
Th
e calcu
latio
n
is:
|
| =
√
5
4
4
3
3
2
2
3
=
√
92
= 9
,
59
17
=
|
|
=
,
=
0
,
5
213
=
|
|
=
,
=
0
,
4
171
=
|
|
=
,
=
0
,
4
171
=
|
|
=
,
=
0
,
3
127
=
|
|
=
,
=
0
,
3
127
=
|
|
=
,
=
0
,
2
086
=
|
|
=
,
=
0
,
2
086
=
|
|
=
,
=
0
,
3
127
Calcu
l
atio
n
s
perfo
r
m
e
d
in
t
h
e sam
e
way so
as to
ob
tain
th
e fo
llo
wi
n
g
results:
R
=
0,
5213
0
,
2341
0,
4171
0
,
2341
0,3015
0,5976
0,3015
0,3585
0,
4171
0
,
2341
0,
3127
0,
3127
0,
2086
0,
2086
0,
3127
0
,
5853
0
,
3512
0
,
3512
0
,
3512
0
,
3512
0,3015
0,4781
0,3015
0,3015
0,6031
0,3015
0,3015
0,2391
0,2391
0,2391
0,2391
0,2391
0,3046
0,1924
0,3046
0,2887
0,
3612
0
,
2673
0,
2407
0
,
2673
0,3046
0,3849
0,4062
0,5077
0,3046
0,4062
0,2031
0,1924
0,2887
0,4812
0,4812
0,3849
0,
3612
0
,
2673
0,
3612
0,
2407
0,
4815
0,
3612
0,
3612
0
,
5345
0
,
5345
0
,
2673
0
,
2673
0
,
2673
Phase
2
. Weigh
tin
g
th
e no
rm
alized
m
a
trix
.
V
=
R
.
W
…
…
⋮
⋯
=
⋯
⋯
⋮
⋯
Calcu
l
atio
n
s
perfo
r
m
e
d
in
t
h
e sam
e
way so
as to
ob
tain
th
e fo
llo
wi
n
g
results:
V
=
2,
0852
1
,
1705
1,
6681
1
,
1705
0,603
2,9881
0,603
1,7952
1,
6681
1
,
1704
1,
2511
1,
2511
0,
8341
0,
8341
1,
2511
2,926
1
,
7556
1
,
7556
1
,
7556
1
,
7556
0,603
2,3905
0,603
0,603
1,2061
0,603
0,603
1,1952
1,1952
1,1952
1,1952
1,1952
0,6092
0,3848
0,6092
0,5774
1,
0836
0
,
8019
0,
7221
0
,
8019
0,6092
0,7698
0,8123
1,0153
0,6092
0,8123
0,4061
0,3849
0,5774
0,9623
0,9623
0,7698
1,
0835
0
,
8018
1,
0835
0,
7223
1,
4446
1,
0835
1,
0835
1
,
6035
1
,
6035
0
,
8018
0
,
8018
0
,
8018
Phase
3
.
Det
e
r
m
i
n
at
i
on o
f
t
h
e
c
onc
o
r
da
nce se
t
usi
n
g t
h
e f
o
l
l
owi
n
g
co
n
d
i
t
i
ons:
,
un
tuk
j
=
1,
2,
3
,
…
n
Th
e calcu
latio
n
is:
,
j=
1,
2,.
.
8 t
h
en
obt
ai
ned
= {1,
2
,3
,4
,5
,7
,8
} mean
s to
m
eet t
h
e cond
itio
n
s
in
t
h
e 1
s
t
,
2n
d,
3
r
d,
4t
h
,
5t
h,
7t
h
dan
8t
h c
o
m
p
ari
s
o
n
s.
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {1,2
,3
,4
,5
,7
,8
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {1,3
,4
,6
,7
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {1,3
,4
,7
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {1,4
,5
,8
}
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
D
ecision
S
uppo
rt
S
y
stem
f
o
r t
h
e
S
e
lectio
n o
f
Co
u
rses in th
e
H
i
gh
er Edu
c
a
t
io
n
u
s
i
n
g th
e
…
(Ma
d
e
Su
darm
a
)
13
3
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {1,3
,4
,7
,8
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {1,3
,4
,5
,7
,8
}
Th
e calcu
latio
n
co
n
tinu
e
d
u
n
til all sets o
f
con
c
ord
a
n
c
e co
m
p
letely o
b
t
ain
e
d
.
Determ
in
atio
n
o
f
th
e d
i
scord
a
n
ce
set u
s
ing
t
h
e fo
llowing
co
nd
itio
ns:
,
un
tuk
j
=
1,
2,
3
,
…
n
Th
e calcu
latio
n
is:
,
j
=
1,2,..8 th
en
ob
tain
ed
=
{6
} m
ean
s to
meet th
e conditio
n
s
in th
e
6th
com
p
arisons.
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {6}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {2,5
,8
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {2,5
,6
,8
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {2,3
,6
,7
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {2,5
,6
}
,
j=
1,
2,
..
8 t
h
e
n
obt
ai
ne
d
= {2,6}
Th
e calcu
latio
n
co
n
tinu
e
d
u
n
til all sets o
f
d
i
sco
r
d
a
n
ce co
mp
letely o
b
t
ain
e
d
.
Phase
4
. C
a
l
c
u
l
at
i
on o
f
m
a
t
r
i
x
of
co
nc
or
dan
ce an
d
di
sco
r
d
a
nce.
∈
Th
e calcu
latio
n
is:
=
+
+
+
+
+
+
= 4
+
5+2+
5+
2+3+
3 =
2
4
=
+
+
+
+
+
+
= 4
+
5+2+
5+
2+3+
3 =
2
4
=
+
+
+
+
= 4+
2
+
5+2+
3 =
1
6
=
+
+
+
= 4+
2+
5+3
=
14
=
+
+
+
= 4+
5+
2+3
=
14
=
+
+
+
+
= 4+
2
+
5+3+
3 =
1
7
=
+
+
+
+
+
= 4+
2+5+
2+
3+3
=
19
Calcu
l
atio
n
s
perfo
r
m
e
d
in
t
h
e sam
e
way so
as to
ob
tain
th
e fo
llo
wi
n
g
results:
C
=
02
4
14
0
24
16
16
13
17
26
17
14
17
17
15
15
17
17
17
15
01
6
15
12
17
17
15
0
18
12
14
16
14
14
16
14
17
19
14
16
16
14
22
0
17
17
21
19
19
0
21
17
17
21
24
21
24
0
22
24
21
22
22
0
The
m
a
t
r
i
x
o
f
di
sco
r
da
nce
i
s
cal
cul
a
t
e
d base
d on
t
h
e se
t of
discorda
nce t
h
at obtained at phase
3, as
follows:
∈
Th
e calcu
latio
n
is:
=
= 2
=
= 2
=
+
+
= 5
+
2
+
3
= 10
=
+
+
+
= 5+
2+
2+3
=
12
=
+
+
+
= 5+
2+
2+3
=
12
=
+
+
= 5+
2+2
= 9
=
+
= 5+
2 =
7
Calcu
l
atio
n
s
perfo
r
m
e
d
in
t
h
e sam
e
way so
as to
ob
tain
th
e fo
llo
wi
n
g
results:
D
=
02
12
0
21
0
10
13
90
9
12
9
9
11
11
9
9
9
11
01
0
11
14
9
9
11
0
8
14
12
10
12
12
10
12
97
12
10
10
12
4
0
9
9
5
7
7
0
5
9
95
2
5
2
0
4
2
5
4
4
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE Vo
l. 5
,
N
o
. 1
,
Febru
a
ry
2
015
:
12
9
–
13
5
13
4
Phase
5
.
Det
e
r
m
i
n
at
i
on o
f
t
h
e
d
o
m
i
nance val
u
e
of
co
nc
or
da
nce a
n
d
di
sc
or
dance
.
Tabl
e
3. T
h
e
d
o
m
i
nance val
u
e o
f
c
onc
or
da
n
ce
Courses (alternativ
e)
Calculation of the do
m
i
nance value
of concor
dance
T
h
e do
m
i
nance value of
concor
dance
I
ndonesian L
iter
a
tur
e
0+24+24+1
6
+14+
14+17+1
9
128
Ancient Javanese
Literatu
re 14+0+16+1
3
+16+
14+14+1
6
103
Literature of Bali
17+
26+0+1
6
+16+
14+17+2
1
127
E
nglish L
iter
a
tur
e
17+15+1
5
+0+22+
19+24+2
4
136
Japanese Literatu
re
14+17+1
2
+18+0+
19+21+2
1
122
Ar
cheology
17+17+1
7
+12+1
7
+
0+24+22
126
Cultur
a
l Anthr
opol
ogy
17+17+1
7
+14+1
7
+
21+0+22
125
Histor
y 15+15+1
5
+16+2
1
+
17+22+0
121
The cal
c
u
l
a
t
i
on
of
t
h
e
d
o
m
i
n
a
nce
val
u
e
o
f
d
i
scor
dance
i
s
a
s
f
o
l
l
o
ws:
Tabl
e
4. T
h
e
d
o
m
i
nance val
u
e o
f
di
sco
r
da
nc
e
Courses (alternativ
e)
Calculation of the do
m
i
nance value
of discor
dance
T
h
e do
m
i
nance value
of discor
dance
I
ndonesian L
iter
a
tur
e
0+2+2+10+1
2
+12
+
9+7
54
Ancient Javanese
Literatu
re 12+0+10+1
3
+10+
12+12+1
0
79
L
iter
a
ture of Bali
9+0+0+10+1
0
+12
+
9+5
55
E
nglish L
iter
a
tur
e
9+11+11+0+
4+7+
2+2
46
Japanese Literatu
re
12+9+14+8+
0+7+
5+5 60
Ar
cheology
9+9+9+14+9+0+
2
+
4
56
Cultur
a
l Anthr
opol
ogy
9+9+9+12+9+5+
0
+
4
57
Histor
y 11+11+1
1
+10+5+
9+4+0
61
Phase 6
.
The
fi
nal
d
o
m
i
nance i
s
t
h
e res
u
l
t
of a
re
duct
i
o
n i
n
t
h
e d
o
m
i
nance
bet
w
een
t
h
e co
nc
or
dan
ce
a
n
d
discorda
nce
va
lue of a
n
alternative.
Tab
l
e
5
.
Th
e resu
lt of th
e m
a
n
u
a
l calcu
lation
Courses (alternativ
e)
The value of
the f
i
nal
do
m
i
nance
Ranked based on t
h
e value of the
final do
m
i
nance
I
ndonesian L
iter
a
tur
e
74
2
Ancient Javanese
L
iter
a
ture
24
8
Literature of Bali
72
3
English Literature
90
1
Japanese L
iter
a
tu
re
62
6
Ar
cheology
70
4
Cultur
a
l Anthr
opol
ogy
68
5
Histor
y 60
7
Tabl
e
6. C
o
m
p
ari
s
o
n
s
of
t
h
e c
a
l
c
ul
at
i
on
res
u
l
t
s
Courses (alternativ
e)
The value of
the f
i
nal
do
m
i
nance (
r
e
sult of
the m
a
nual
calculation)
The value of
the f
i
nal
do
m
i
nance (
r
e
sult of
the
calculation of appl
ication)
Ranked based on t
h
e
value of the final
do
m
i
nance
I
ndonesian L
iter
a
tur
e
74
74
2
Ancient Javanese
L
iter
a
ture
24
24
8
Literature of Bali
72
72
3
E
nglish L
iter
a
tur
e
90
90
1
Japanese L
iter
a
tu
re
62
62
6
Ar
cheology
70
70
4
Cultur
a
l Anthr
opol
ogy
68
68
5
Histor
y 60
60
7
The com
p
ari
s
ons
res
u
l
t
a
t
Tabl
e 6 s
h
o
w
s t
h
e cal
cul
a
t
i
on p
r
oc
ess
of
deci
si
on
m
a
ki
ng
usi
n
g
applications capable of ge
nerating output that has a
very good level of ac
curacy
and in accorda
n
ce with t
h
e
rules
of calcula
tion
of ELECT
R
E m
e
thod.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
D
ecision
S
uppo
rt
S
y
stem
f
o
r t
h
e
S
e
lectio
n o
f
Co
u
rses in th
e
H
i
gh
er Edu
c
a
t
io
n
u
s
i
n
g th
e
…
(Ma
d
e
Su
darm
a
)
13
5
4.
CO
NCL
USI
O
N
The use of E
LECTRE
m
e
thod in th
e ap
pl
i
cat
i
on o
f
de
ci
si
on su
p
p
o
r
t
sy
st
em
for t
h
e sel
ect
i
on of
courses in c
o
llege is ve
ry effective and
rele
vant. T
h
is
is because the E
L
ECTRE m
e
thod
is able to
process the
in
pu
t d
a
ta
b
y
u
s
ing
a
relatively sh
ort calculatio
n
and
is ab
le to
g
e
n
e
rate ou
tpu
t
d
a
ta as exp
ected
, tak
i
n
g
in
to
account the a
d
vanta
g
es a
nd
drawbac
k
s of
ea
ch alternative
(courses).
Out
p
ut data resulting from
calculations
usi
n
g E
LEC
T
R
E m
e
t
hod i
s
al
so
prese
n
t
e
d
i
n
t
h
e
fo
rm
of rat
i
ng
, m
a
ki
ng
i
t
easi
e
r
f
o
r
users
t
o
anal
y
ze t
h
e
syste
m
o
u
t
pu
t
an
d d
e
term
in
e th
e courses t
h
at su
itab
l
e to b
e
ch
osen.
ACKNOWLE
DGE
M
ENTS
I w
o
ul
d l
i
k
e
t
o
e
x
p
r
ess m
y
very
great
a
p
p
r
eci
at
i
on t
o
go
es t
o
c
o
l
l
eag
u
e
w
h
o
has
m
a
de
val
u
a
b
l
e
co
n
t
ribu
tio
n
s
i
n
th
is stud
y and
th
ei
r critical
co
mmen
t
s on
t
h
is m
a
n
u
s
cri
p
t.
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