 Open Access
 Total Downloads : 284
 Authors : Mesran, Suginam, Garuda Ginting, Robbi Rahim
 Paper ID : IJERTV6IS020074
 Volume & Issue : Volume 06, Issue 02 (February 2017)
 Published (First Online): 04022017
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Implementation of Elimination and Choice Expressing Reality (ELECTRE) Method in Selecting the Best Lecturer (Case Study STMIK BUDI DARMA)
Mesran1
Department Computer Engineering, STMIK Budi Darma Medan,
Jln. Sisingamangaraja No. 338 Telp 0617875998,
Garuda Ginting 2
Dosen Tetap STMIK Budi Darma Medan
Jln. Sisingamangaraja No. 338 Telp 0617875998,
Suginam3
Department Computer Engineering, STMIK Budi Darma Medan,
Jln. Sisingamangaraja No. 338 Telp 0617875998,
Robbi Rahim4
Departement of Computer Engineering Medan Institute of Technology
Jl. Gedung Arca No.52 Kota Medan, Sumatera Utara,
Abstract Measurement of the performance index lecturer at a college should be obliged to do. Performance indexes the basis

Lecturer

THEORY
for university lecturer in determining the best lecturer owned by the university. The right solution to the problems faced by university leaders as a decision maker to consider several criteria relating to the determination of the best computer lecturer, in this case the determination of best computer lecturer using ELECTRE method
Keywords Decision making, MultiCriteria Decision Making, MultiCriteria Decision Making, Best Lecturer, ELECTRE

INTRODUCTION
The lecturer is the one resource that must have either PTN or PTS. Lecturers who have a good performance will improve the quality of universities. For faculty performance can be enhanced, it is no doubt anymore that universities must be able to assess the extent to which performance of the lecturers, so they know the performance generated by faculty lecturer at the college. For lecturers who earn top performance ratings. Certainly, the senior lecturers are the best that have high ratings on performance
accomplishments. In conducting the selection of best lecturers can use decision support system, which has many
Under Law 14, 2015, which referred to the lecturers are professional educators and scientists with the primary task of transforming, developing and disseminating science, technology, and the arts through education, research, and community service [7].



Elimination and Choice Expressing Reality (ELECTRE) ELECTRE methods introduced by Roy (1966), which uses a comprehensive evaluation approach by trying to create a ranking of the number of each alternative described on some criteria [8].
The steps of the method ELECTRE can see as below [6] [9] [10]:
Step 1: Preparing for Decision Matrix
In the column, there is a decision matrix criteria (n) and the row in the form of alternative (m). The initial stage and the base for processing to decision support
x11 x12 x13 … x1n
methods that can apply to Weight Product (WP), Analytics Hierarchy Process (AHP), Simple Addictive Weighting (SAW) and some other methods [1] [2]. Decision support
x
x 21
ij .
x
m1
x21
.
xm2
x22
.
xm3
…
…
…
x2n
.
x
mn
(1)
systems should have some alternative, criteria, and weighting that is a crucial factor in the decision support system [1] [3] [4].
Step 2: Normalizing the Decision Matrix
Decision matrix will be normalized by using the following formula and produces the normalized model.
Based on the above, this research is to apply the method Elimination and Choice expressing Reality (ELECTRE) in making decisions for determining best computer lecturer of STMIK Budi Darma with the criteria used, ie rank (C1), research (C2), scientific publications (C3) , dedication (C4), supporting element (C5). Another variant of the ELECTRE approach is TOPSIS method [5] [6].
rij
xij
x
m
2
ij
i1
i=1,2,,m (2)
j=1,2,,n
For cost parameters using the following equation.
max{ v v }
d kj lj jDkl
kl
(11)
1 max{ vkj vlj } j
rij
rij
m 2
i=1,2,,m (3)
d matrix is also a dimension of m x m and did not take the value of the l column and k row, d array as below.
1
i1 rij
j=1,2,,n
d12
…
d1n
d
… d
The results of processing the normalized decision matrix, as shown below.
d 21
.
d
m1
.
dm2
2n
.
…
(12)
r11
r21
rij
r12 r21
r13 r22
…
…
r1n
r2n
(4)
Step 6: Determine the dominant concordance matrix and discordance
. .
r r
. … .
r … r
This array could construct with the aid of a threshold value (threshold) c. The formula can obtain c value.
m1 m2 m3 mn
Step 3: Giving weight value
Furthermore, decision makers provide interest factor
m m
c
kl (13)
c k 1 l 1
(weight) on each of the criteria which express its relative importance (wj).
m(m 1)
W= (w1, w2, … , wn) ;
n
wj 1
j 1
(5)
The alternative Ak can have the opportunity to dominance A1 if the concordance index ckl exceed the threshold c with ckl c and elements of the dominant F concordance matrix defined as:
Step 4: Calculate the normalized weighted matrix
Each column of the r matrix multiplied by the weights (wj)
fkl
1 , if ckl c
0
, if ckl < c (14)
determined by the decision maker, can be seen below.
The same also applies to the dominant discordance matrix G with threshold d. The following formula can obtain d value:
vij wj .rij
(6) m m
Where v is
dkl
d k 1 l 1
(15)
v11 v12
…
v1n
m(m 1)
vij
v
21
.
v
v22
.
v
…
.
…
v2n
.
v
(7)
The elements of the dominant F discordance matrix defined as:
m1 m2
mn
1 , if d d
Step 5: Determining the set of concordance and discordance index
The set of concordance index {ckl} indicates where the sum of weighted criteria Ak alternatively is better than the alternative A1.
Ckl={jvkj vlj} with j=1,2,..,n (8) The set of discordance index {dkl} given as follows: Dkl={jvkj<vlj} with j=1,2,..,n (9)
Step 5: Calculating concordance and discordance matrix
To calculate or determine the value of the elements in the concordance model is by adding weights are included in the set of concordance
gkl kl
0 , if dkl < d (16)
Step 7: Determining aggregate dominance matrix
The model e as total dominance matrix is a matrix which each element is the multiplication between the matrix elements f, and g corresponding form elements.
ekl=fkl x gkl (17)
Step 8: Elimination of the less favorable alternative
The matrix e gives the preferred order of each option, ie when ekl = 1 then the alternative is Ak better alternative than the A1. That the rows in a matrix e which has a total of at least ekl = 1 can be eliminated.

RESULT & DISCUSSION
ckl wj
jCkl
(10)
The initial step to find the best lectures which provide 5
criteria and three alternatives. The criteria and the weights show in Table and choices could see in Table II.
To determine the value of the elements in the discordance
matrix is by dividing the maximum difference of criteria including into subsets discordance with the highest difference between the value of all existing criteria
Table I. The Criteria and Weights
k=2 i=1 c21
Criteria (C) 
Weights(W) 
Rank (C1) 
0.35 
Research (C2) 
0.15 
Scientific Publications (C3) 
0.2 
Dedication (C4) 
0.2 
Supporting Element (C5) 
0.1 
j=1 if v21 v11 0.1485 0.1980 no j=2 if v22 v12 0.0739 0.0923 no j=3 if v23 v13 0.1193 0.1491 no
j=4 if v24
v14
0.0970 0.1455 no
Table II. Alternative of Lecturer
Name of Lecturer (A) 
C1 
C2 
C3 
C4 
C5 
5 
3 
4 
4 
2 

A1 
4 
5 
5 
3 
4 
A2 
3 
4 
4 
2 
4 
A3 
5 
5 
2 
2 
2 
By using equation (2), then in the process of normalization matrix.
j=5 if v25 v15 0.0667 0.0667 yes then j=5 c21={5}
i=2 c22 = identity j=1,2,3,4,5 i=3 c23
j=1 if v21 v31 0.1485 0.2475 no j=2 if v22 v32 0.0739 0.0923 no
j=3 if v23 v33 0.1193 0.0596 yes then j=3 j=4 if v24 v34 0.0970 0.0970 yes then j=4 j=5 if v25 v35 0.0667 0.0333 yes then j=5
x1
42 32 52 7.7011
x2
52 42 52 8.1240
c23={3,4,5}
r x11 4 0.5657
r12 x12 5 0.6155
k=3 i=1 c
x
1
11 7.7011 x2
8.1240 31
r x21 3 0.4243
r x22 4 0.4924
j=1 if v31 v11 0.2475 0.1980 yes then j=1
21
r31
x1
x31
x1
7.7011
5
7.7011
0.7071
22
r32
x2 x32 x2
8.1240
5
8.12401
0.6155
j=2 if v32 v12 0.0923 0.0923 yes then j=2 j=3 if v33 v13 0.0596 0.1491 no
j=4 if v34 v14 0.0970 0.1455 no
So on the same way, it will get normalized r matrix :
j=5 if v35 v15 0.0333 0.0667 no
c31 = {1,2}
0.5657
r 0.4243
0.7071
0.6155
0.4924
0.6155
0.7454
0.5963
0.2981
0.7276
0.4851
0.4851
0.6667
0.6667
0.3333
i=2 c32
j=1 if v31 v21 0.2475 0.1485 yes then j=1 j=2 if v32 v22 0.0923 0.0739 yes then j=2
Matrix V is calculated based on the equation (6) as follows: v11 = w1r11 = (0.35) (0.5657) = 0.1980
v12 = w2r12 = (0.15) (0.6155) = 0.0923
v13 = w3r13 = (0.2) (0.7454) = 0.1491
v14 = w4r14 = (0.2) (0.7276) = 0.1455
v15 = w5r15 = (0.1) (0.6667) = 0.0667
From the above results obtained matrix v as follows:
j=3 if v33 v23 0.0596 0.1193 no
j=4 if v34 v24 0.0970 0.0970 yes then j=4 j=5 if v35 v25 0.0333 0.0667 no
c32 = {1,2,4}
i=3 c33 = Identity j=1,2,3,4,5
Then for dkl value can synchronize with a value that is not contained in ckl to set of dkl
0.1980
v 0.1485
0.2475
0.0923
0.0739
0.0923
0.1491
0.1193
0.0596
0.1455
0.0970
0.0970
0.0667
0.0667
0.0333
d12={} d23={1,2}
d13={1} d31={3,4,5}
d21={1,2,3,4} d32={3,5}
Next calculate the set of concordance index:
k=1 i=1 c11=identity j=1,2,3,4,5 i=2 c12
j=1 if v11 v21 0.1980 0.1485 yes then j=1 j=2 if v12 v22 0.0923 0.0739 yes then j=2 j=3 if v13 v23 0.1491 0.1193 yes then j=3 j=4 if v14 v24 0.1455 0.0970 yes then j=4 j=5 if v15 v25 0.0667 0.0667 yes then j=5 c12={1,2,3,4,5}
i=3 c13
j=1 if v11 v31 0.1980 0.2475 no
j=2 if v12 v32 0.0923 0.0923 yes then j=2
The member states set of dij columns on vij
Then the concordance matrix is formed. ckl element is calculated by equation (10)
c12 = w1+w2+w3+w4+w5 = 0.35+0.15+0.2+0.2+0.1=1 c13 = w2+w3+w4+w5 = 0.15+0.2+0.2+0.1 = 0.65
c21 = w5 = 0.1
c23 = w3+w4+w5 = 0.2+0.2+0.1 = 0.5 c31=w1+w2 = 0.35+0.15 = 0.5
c32 = w1+w2+w4 = 0.35+0.15+0.2 = 0.7
The concordance matrix is:
j=3 if v13 v33 0.1491 0.0596 yes then j=3 j=4 if v14 v34 0.1455 0.0970 yes then j=4 j=5 if v15 v35 0.0667 0.0333 yes then j=5 c13={2,3,4,5}
1
C 0.1
0.5 0.7
0.65
0.50
At concordance model the elements of dkl are calculated
0
0 A1
based on the equation (11) as follows:
E 0
0 A
2
d12
max{0} 0 0
0
1
A3
max{0.0495;0.0185;0.0298;0.0485;0}
0.0495
With the ELECTRE methods of calculation indicated ekl=1 then the alternative is better than the A1. Alternative A3
d13
max{0.0495}
max{0.0495;0;0.0894;0.0485;0.0333}
0.0495
0.0894
0.5534
better than A2, but not necessarily be able to draw connections between A1 to A2 and the A1 and A3.

CONCLUSION
In this case using ELECTRE method to solve the problem of
d21 max{0.0495;0.0185;0.0298;0.0485} 0.0495 1
selecting the best computer lecturer. The results of the study
d23
max{0.0495;0.0185;0.0298;0.0485;0}
max{0.0495;0.0185}
max{0.0990;0.0185;0.0596;0;0.0333}
0.0495
0.0495 0.5
0.0990
are expected to be useful for decision makers in STMIK Budi Darma, especially for policy makers. Selection of the best computer lecturer believed to be able to motivate the lecturers at the university, so the faculty performance becomes better. Application of this method by using data
d31 max{0.0894;0.0485;0.0333} 0.0894 1
from a sample of cases contained in STMIK Budi Darma.
d32
max{0.0495;0;0.0894;0.0485;0.0333}
max{0.0596;0;0.0333}
max{0.0990;0.0185;0.0596;0;0.0333}
0.0894
0.0596 1
0.0596

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