Implementation of the Weighted Product Method in the Best Student
Selection Decision Making System Application
Murdani
1
, Fricles Ariwisanto Sianturi
2
, Harvei Desmon Hutahaean
2
, Sony Bahagia Sinaga
1
and
Denni M. Rajagukguk
3
1
AMIK Stiekom Sumatera Utara, Medan –Indonesia
2
STMIK Pelita Nusantara Medan, Medan –Indonesia
3
STMIK Kristen Neumann Indonesia, Medan –Indonesia
Keywords: Best Student Selection, Decision Support System, Weighted Product
Abstract: Decision support systems are information systems that are interactive and provide information,
data modeling and manipulation techniques, and help decision makers in semi-structured and
unstructured conditionals. School is an institution or an effective place to transforms knowledge
from educators to students and as a means of education. Here students get additional knowledge
and moral formation. One of the school assignments is to produce students who are able to
advance the nation. The selection of the best students is carried out in a period with the aim of
stimulating enthusiasm in each student to continue to improve their learning achievement in
school. The best student selection is done by determining a student who is recommended by the
teacher based on the criteria determined by the school. In order for the desired results to be
maximized and appropriate in the process of selecting the best students with using the Product
Weighting Method (WP).
1 INTRODUCTION
School is one of the effective places for science
transformation activities and also educational
facilities for students. Here students will get the
provision of knowledge such as guidance. The
National Education System stipulates that the world
of national education has the duty to develop
capabilities and shape the character of the nation's
civilization and aims to develop the potential of
students to become human beings who have faith
and piety to God Almighty, have good morals, are
knowledgeable, creative, independent and become a
democratic citizen and has integrity and
responsibility.
One of the criteria for being able to advance the
nation is providen the best student. The selection of
the best students must be done periodically and
continuously with the aim that students always
encourage their enthusiasm to continuously improve
their learning achievement. The process of selecting
the best students is not an easy matter (Kusrini &
Kom, 2007). The problem in the process of selecting
the best students is done by choosing one student
recommended by the teacher whose criteria have
been determined by the school, but the desired
results are not maximal due to inaccuracies data in
the selection process so the results have an impact
on the results of the decisions given inappropriately.
Should to determine the best students needed the
right formula, fast and fair according to the
development of science and technology today, in
making a decision support system for selecting the
best students who can do calculations quickly, to
help, accelerate and simplify the decision making
process. One of the decisions that can be used in
selecting the best students is to use the Fuzzy
Multiple Attribute Decission Making (FMADM)
model, which is a model used to find the best
alternative from a number of alternatives based on
multi criteria (Itik, Alici, Ilkhchi, & Moallemi, n.d.)
(Sigit & Kapuji, 2014).
Weighted Product Method (WP) is a way of
solving problems in making a decision. The use of
methods in decision support systems must have the
correct criteria, besides that it must determine the
importance of each criterion. WP method requires a
Murdani, ., Sianturi, F., Hutahaean, H., Sinaga, S. and Rajagukguk, D.
Implementation of the Weighted Product Method in the Best Student Selection Decision Making System Application.
DOI: 10.5220/0009495900990104
In Proceedings of the 1st Unimed International Conference on Economics Education and Social Science (UNICEES 2018), pages 99-104
ISBN: 978-989-758-432-9
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
99
normalization process because this method will
multiplying the results of the assessment of each
attribute. The results of the multiplication have not
been meaningful if not compared (divided) with
standard values. The weight for the benefit attribute
will function as a positive rank in the multiplication
process, while the cost weight will function as a
negative rank.
The purpose of the study is to explain the process
of selecting the best students by applying the WP
method in the best student decision support decision
system as an alternative to assist the headmaster in
determining the best student selection and can
optimize performance through the existing system.
2 RESEARCH METHODS
The research methods carried out in the
implementation of this study are (Kusrini & Kom,
2007):
1. Data Collection
Data collection used is reading and collecting
scientific books and journals related to decision
support systems using the Weighted Product
(WP) method.
2. Troubleshooting
Studing and understanding the theoretical basis
associated with the problems to be discussed.
3. Testing Problems
The use of a method in developing the right
system and adapted to the problem.
1. Implementation of the Problem
The implementation phase is carried out starting
from the coding of programs to implement all
the design of decision support systems that
already use the programming language.
2. Making Reports
Preparation of Research Reports is a stage
where reporting all things and data that have
been done during the research in preparing the
Research report.
3 THEORICAL FOUNDATION
3.1 Decision Support Systems
Decision support system is a settlement tool
interactive in information system that provides data /
information, modeling, and manipulating data
(Nofriansyah & Defit, 2017). This system is used to
help decision makers in semiterstructured and
unstructured decision situations, where no one
knows exactly how decisions should be made.
Decision Support System Application or DSS
uses data, presents interface design for easy users,
and combines the ideas and thoughts of the decision
maker (Limbong, Simarmata, Fauzi, et al., 2018).
DSS will be more intended to support management
in carrying out analytical work in situations that are
less structured and with unclear criteria(Limbong,
Simarmata, Sriadhi, et al., 2018).
The objectives of DSS are:
1. Helping a manager for making decisions on
semi-structured problems is also unstructured.
2. Providing a basis for support for all
consideration of managers, but not intended to
replace the role and function of a manager
3. Increasing the effectiveness of decisions taken
by managers is more than an improvement in
efficiency
4. Computational speed. Computers provide
opportunities for decision makers to use
computational formulas quickly, precisely at
very low costs
5. Increased productivity.
6. Quality support. Computer technology can
improve the quality of decisions made. For
example, the more data accessed, the more
alternatives can be evaluated.
7. Competitive. Management and optimization of
company resources. Very high competitiveness
will make decision-making tasks difficult.
8. Overcoming cognitive limitations in processing
and storage. The human brain has limited ability
to process, and produce output in the form of
information.
3.2 Weighted Product (WP) Method
Weighted product method is a method in
determining a decision by doing a process and
connecting each attribute rating, where the rating of
each attribute is raised first with the weight of the
attribute in question (Wang, Liu, Wang, & Lai,
2010) (Alfita, Teknik, Trunojoyo, & Product, n.d.).
This process is called the normalization process. The
preference for alternative Ai is given as follows:
a. Determination of W weight value
…………………(2.1)
b. Determination of the value of Vector S
S = ( Wij
Awj
. w) . ( Win
Awn
. w) …. (2.2.)
c. Determines the value of Vector V

…………………(2.3)
Where :
V = Alternative preferences are considered as vector
V
UNICEES 2018 - Unimed International Conference on Economics Education and Social Science
100
W = Weight of criteria / subcriteria
j = Criteria
i = Alternative
n = Number of criteria
S = Alternative preference analogous to vector S
The steps in calculating the Weighted Product
method are as follows:
1. Multiplying all attributes for all alternatives
with the weight as a positive rank for the cost
attribute.
2. The multiplication result is summed to produce
a value for each alternative
3. Divide the value of V for each alternative with
the value of each alternative
4. Found the best alternative sequence that will be
a decision.
4 RESULT AND DISCUSSION
4.1 Problem Analysis
From observations and field studies it is known that
the selection of the best students is still mostly done
manually, in the sense that it has not utilized the
computer's capabilities as a whole, so there are still
some problems found (Nofriansyah & Defit, 2017)
(SITORUS & Wardoyo, 2004), including:
1. Difficulties in presenting teacher assessment
results data in a fast time.
2. Difficulties in filing all the results of the
assessment from one period, for material
evaluation to the next period in the selection of
the best students.
The requirements that have been determined by the
school in the selection of the best students, namely
as follows:
1. Average value
2. Attitude
3. Knowledge
4.2 Implementation of Weighted Product
Methods
In choosing the best student to be one of the students
who is entitled to be the best student with students
who have met the criteria set by the school. As a
researcher sample the author included 5 data
students who would be selected as the best students.
With the attached data applied the WP (Weighted
Product) method, the necessary criteria and weights
in performing calculations so that the best
alternatives will be obtained are as follows:
a. Determining each of each criterion can be seen
in table 1:
Table 1: Description of Criteria
Criteria
Explanation
C1
Report Card
Score Average value of the overall odd and even number of
students
C2
List of attendees
Amount of student absence
C3
Attitude
The level of behavior of a student
C4
Skills
Level of student activity
C5
Achievement
Level of student achievement
b. Furthermore, the decision maker gives the
Preference Weight for each criterion as W shown in
table 2:
Table 2: Determination of W Value
Criteria
Range (%)
Weight
C1
25
0,25
C2
25
0,25
C3
20
0,20
C4
15
0,15
C5
15
0,15
c. From each of these criteria will be determined the
weights. The weight consists of five fuzzy numbers,
which are Very Bad (VB), Not Good (NG),
Sufficient (S), Fairly Good (FG), and Very good
(VG) as shown in Figure 1:
Figure 1: Weight of Criteria
Information :
Very bad = 0
Not Good = 0.25
Implementation of the Weighted Product Method in the Best Student Selection Decision Making System Application
101
Fufficient = 0.50
Fairly Good = 0.75
Very Good = 1
d. Determining criteria value data can be seen in
table 3:
Table 3: Original Student Data
Alternative
Criteria
Report Card
List of
attendees
Attitude
Skills
Achievement
A
1
84,325
2
B
B
Nothing
A
2
84.855
0
B+
B+
Nothing
A
3
83,555
3
B
B
Nothing
A
4
82,52
2
B-
B
Nothing
A
5
86,19
2
A
A
Exist
The Fuzzy Weighting is as follows:
a. Report Card Value Criteria (C1)
In the value variable the skills consist of five fuzzy
numbers, namely Very Good, More than Good,
Good, More than Enough, Not Good. As shown in
the following table:
Table 4: Determines Report Card Criteria
Report
Card
Fuzzy Criteria
Weight
90-99
Very Good
1
80 -89
More than Good
0,75
70 – 79
Good
0,50
60 -69
More than Enough
0,25
≤ 59
Not Good
0
b. Attendance Criteria (C2)
In the variable Attendance Value consists of five
fuzzy numbers, namely Very Good, Good Enough,
Good, Not Good, Bad. As shown in the following
table:
Table 5: Determines Attendance Criteria
List of
attendees
Fuzzy Criteria
Weight
0
Very Good
1
1
Good Enough
0,75
2
Good
0,50
3
Not Good
0,25
>4
Bad
0
c. Attitude Criteria (C3)
The Attitude Value variable consists of five yatu
fuzzy numbers Very not good (C), Poor (B-),
Sufficient (B), Fairly Good (B +), Very Good (A),
As shown in figure 6:
From the picture above Crisp numbers can be
converted. For more details, it can be formed in table
6:
Table 6: Determines Attitude Value Criteria
Attitude
Value
Fuzzy Criteria
Weight
A
Very good
1
B+
Pretty good
0,75
B
Good
0,50
B-
Not good
0,25
C
Very not good
0
d. Skill Criteria (C4)
The value variable consists of five fuzzy numbers,
namely enough (C), more than enough (C +), good
(B), more than good (B +), very good (A). As shown
in figure 5:
Table 7: Determining Skills Criteria
Skill
Value
Fuzzy Criteria
Weight
A
Very good
1
B+
Pretty good
0,75
B
Good
0,50
B-
Not good
0,25
C
Very not good
0
e. Achievement Criteria (C5)
Table 8: Determines Achievement Criteria
Achievement
Weight
Any
0,75
Nothing
0,25
Match rating of each student. Based on alternative
data above, a suitability rating of each alternative
can be formed on each criterion, shown in table 9:
UNICEES 2018 - Unimed International Conference on Economics Education and Social Science
102
Table 9: Compatibility Rating
Alternative
Criteria
Report Card
List of
attendees
Attitude
Skills
Achievement
A
1
0,75
0,50
0,50
0,50
0.25
A
2
0,75
1
0,75
0,75
0.25
A
3
0,75
0,25
0,50
0,50
0.25
A
4
0,75
0.50
0,25
0,50
0.25
A
5
0,75
0,50
1
1
0,75
Previously we have made weight repairs so that
ΣW = 1,
then the calculation is obtained as follows:
25,0
1
25,0
0,150,150,200,250,25
25,0
1
W
25,0
1
25,0
0,15 0,150,200,250,25
25,0
2
W
20,0
1
20,0
0,15 0,150,200,250,25
20,0
3
W
15,0
1
15,0
0,150,150,200,250,25
15,0
4
W
15,0
1
15,0
0,150,150,200,250,25
15,0
5
W
Formation of vector S Then calculated by the
equation as follows:
S = ( Wij
Awj
*W) * (Win
Awn
* W)
S
1
= (0,75
0,25
) * (0,50
0,25
) * (0,50
0,20
) *
(0,50
0,15
)*(0,25
0,15
)
= 0,9306 * 0,8408 * 0,8705 * 0,9012*0,8122
= 0,49854
S
2
= (0,75
0,25
) * (1
0,25
) * (0,75
0,20
) *
(0,75
0,15
)*(0,25
0,15
)
= 0,9306* 1 * 0,9440 * 0,9577*0,8122
= 0,68332
S
3
= (0,75
0,25
) * (0,25
0,25
) * (0,50
0,20
) *
(0,50
0,15
)*(0,25
0,15
)
= 0,9306 * 0,7071 * 0,8705 * 0,9012*0,8122
= 0,41928
S
4
= (0,75
0,25
) * (0,50
0,25
) * (0,25
0,20
) *
(0,50
0,15
)*(0,25
0,15
)
= 0,9306 * 0,8408 * 0,7578 * 0,9012*0,8122
= 0,43401
S
5
= (0,75
0,25
) * (0,50
0,25
) * (1
0,20
) *
(1
0,15
)*(0,75
0,15
)
= 0,9306 * 0,8408* 1 * 1* 0,9577
= 0,74935
The value of vector V is used for ranking and is
calculated by the equation as follows:
Si
Vjn =
∑Si
17904,0
7845,2
0,49854
1
V
2454,0
7845,2
0,68332
2
V
15058,0
7845,2
0,41928
3
V
15587,0
7845,2
0,43401
4
V
26911,0
7845,2
0,74935
5
V
Based on calculations above the decision support
system in selecting the best students, the best value
is the chosen alternative can be seen in Table 10:
Table 10: Ordering Results and WP Method Calculation
No
Student's name
Rank
Value of Calculation
Results
1
(A5) Marisa Nurul Atika
1
0,26911
2
(A2) M. Khairul Hayat Tarigan
2
0,2454
3
(A1) Prasetyo
3
0,17904
4
(A4) Yuliana Safitri
4
0,15587
Implementation of the Weighted Product Method in the Best Student Selection Decision Making System Application
103
5
(A3) Herdiani Syahputri
5
0,15058
Based on the results of calculations and results of
sorting out the WP method above that students who
are selected to be the best students are looking at the
highest score, the best choice is the A5 alternative,
Marisa Nurul Atika with a value of 0.26911 who is
the best student.
5 CONCLUSION
From the results of the analysis and discussion that
has been carried out, the conclusions can be taken as
follows:
1. The technique for implementing the best student
selection is determined based on conditions
such as report cards, attendance, attitudes, skills
and achievements.
2. The process of selecting the best students in this
study uses the Weighted Product (WP) method
which helps in making decisions from several
alternatives that must be taken with the criteria
into consideration.
3. There are six forms in this system: login form,
admin main menu form, student data input
form, criteria weighting form, WP method
process form. So that it can facilitate the school
in the process of selecting the best students and
the data produced is more effective and
efficient.
4. This decision support system needs to be
developed with other methods such as Fuzzy
Multi Criteria Decission Making (FMCDM),
Weighted Product (WP) and others.
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