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Selection of Educational Software Tools based on
AHP and TOPSIS
Aliu Folasade Mercy
Department of Computer Science
Elizade University
Ilara-Mokin, Nigeria
Kehinde K. Agbele
Department of Computer Science
Elizade University
Ilara-Mokin, Nigeria
Abstract— Several software packages are available for the
smooth-running of an educational system. However, making
the choice for most-appropriate software can be very tasking.
This may require the meeting of the school’s management team
to take quality decisions based on some given criteria. As a
result, conflicts may arise if standard multicriteria decision
methods are not applied in the selection process. This current
research focuses on the application of AHP and TOPSIS
methods to select the most relevant software among three
options for use in a high school. Five members of the
management team evaluated the various criteria and
conducted pair-wise comparisons to determine the weights
using AHP. The choices were further ranked based on the
TOPSIS method. The result showed SMS C as the best choice
for the school, with a TOPSIS ranking score of 0.730044.
Keywords— Analytic Hierarchy Process (AHP), Technique
for Order of Preference by Similarity to Ideal Solution (TOPSIS),
School Management Software (SMS), Multicriteria Decision
Making (MCDM)
I. INTRODUCTION
School authorities are constantly involved in several
activities to efficiently manage the educational processes
within their domain. These activities range from admissions
to class work and results-computation among others. The
educational system is gradually transcending from paper
works to digital processes. Therefore, to improve the
performance of the school administration, many school
authorities opt-out a school management software to
smoothly automate some of the important educational
processes. School management software is a collection of
relevant application programs that organizes the school
processes into a well-structured system to effectively give
the staff and students an interesting educational experience.
There are various educational software packages available
nowadays, with each of them having its uniqueness.
Therefore, selecting suitable software for a specific school
can be quite demanding. The application of a multicriteria
decision making (MCDM) method is a good way to make an
optimal choice in the selection process. MCDM methods
include but are not limited to Analytic Hierarchy Process
(AHP), Analytic Network Process (ANP), ELimination Et
Choix Traduisant la REalitè (ELECTRE), Best-Worst
Method (BWM) [1] [2], Technique for Order of Preference
by Similarity to Ideal Solution (TOPSIS) and Preference
Ranking Organization Method for Enrichment Evaluations
(PROMETHEE). There are no specific rules to be followed
when making a choice at MCDM [3]. This work applies
AHP and TOPSIS in the selection of suitable school
management software for an educational system.
II. A REVIEW OF RELATED WORKS
AHP is MCDM tool that can be applied in a wide range
of fields [4]. AHP was originally developed by Thomas L.,
Saaty in the 1970s and has been used in various fields to
make decisions. A multicriteria analysis for supplier
selection in a university using AHP was performed by [5].
The selection was conducted based on flexibility, delivery,
variety, quality and cost. The developed model can evaluate
and monitor the performances of various suppliers for the
procurement department of the institution. In [4], the factors
affecting the choice of retail pharmacies in Bangladesh
while purchasing the drugs from various pharmaceutical
companies were prioritized. Six factors were identified and
then analyzed through analytic hierarchy process (AHP) to
quantify the qualitative factors through a standard scale.
AHP was also used by [6] to develop a framework that
selects suppliers for qualitative dairy products in Indonesia.
The criteria for the analysis were based on quality, quantity,
delivery, warranty, and pricing. In 2019, [7] proposed a
hybrid decision-making approach based on Analytic
Hierarchy Process (AHP) and Dempster-Shafer Theory
(DST) to evaluate and select a new product. AHP and DST
were used in weight determination to improve accuracy and
objectivity.
Furthermore, AHP was applied by [8] in the selection of
an optimal Electronic Toll Collection (ETC) method. Three
ETC technologies were analyzed based on five criteria and
their result provides an intelligent guide for toll selection in
Nigeria. In addition, the application of AHP was used to
establish a multicriteria-based equipment selection
framework for sustainability in the context of the Malaysian
construction industry. The resultant procurement index
helps decision-makers in the process of the acquisition of
sustainable construction equipment in Malaysia [9]. AHP
and ELECTRE methods were used by [10] to evaluate and
select a suitable hospital management software. Their result
showed the cost criterion to be the most important criterion
in the decision-making process.
In [11], Fuzzy TOPSIS was used to quantify data and
prioritize criteria for enterprise information security
architecture. AHP and TOPSIS were integrated by [12] to
determine the most appropriate tomography equipment.
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181http://www.ijert.org
IJERTV11IS060347 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
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648
AHP was used to determine the weights while TOPSIS was
applied to evaluate the purchase options.
A multi-criteria decision approach was applied by [13] to
select the best college coaches. In their work, the AHP was
applied to find the best coaches from different sports and to
rank these coaches while TOPSIS method was used to test
the correctness and effectiveness of the model. The work of
[14] AHP and TOPSIS independently to assess the
performance of some financial institutions and to determine
the best performing organization for a period of four years.
In [15], an experiment was conducted on employee
placement using several MCDM methods. Their results gave
an accuracy of 95% using TOPSIS method. They concluded
that when there are many criteria, the accuracy is reduced
using AHP.
III. METHODOLOGY
A. Analytic Hierarchy Process (AHP)
AHP organizes criteria and alternatives into levels of
hierarchy to enable experts to make easy comparisons
among several variables [16]. In the AHP method, an
important indicator is the number of criteria. This affects the
consistency of the result because more than seven criteria
lead to an increase in inconsistency [17]. The AHP
mathematical methods are shown in equations (1) and (2).
– Define the value of the criteria, that is, the
judgment matrix, C, on the scale 1–9.
– Calculate normalized matrix using Equation (1):
(1)
where Cij is the criteria value; ∑Cij is the column sum.
– Calculate priority vector using Equation (2):
(2)
where ∑Xi,j is the normalized matrix column sum; and n is
the number of criteria.
When applying AHP method, it is important to involve
experts in the evaluation process so that the values obtained
can be re-used in future [18]. The decision process is done
using Saaty’s scale of preference as seen in Table I.
TABLE I. Scale of Comparison [19]
Scale
Degree of Preference
1
Equal significance
3
Moderate significance of a factor over the other
5
Strong significance
7
Very strong significance
9
Ultimate importance
2, 4, 6, 8
Estimates for inverse comparison
B. Technique for Order of Preference by Similarity to
Ideal Solution (TOPSIS)
The principle used in TOPSIS is that the chosen
alternative must have the closest distance from an ideal best
solution and furthest from an ideal worst solution from a
geometric point of view using the Euclidean distance to
determine the relative proximity of an alternative with the
optimal solution. The positive ideal solution, , is defined
as the sum of all the best attainable values for each attribute,
while the negative ideal solution, , consists of all the
worst values achieved for each attribute [15] [20].
TOPSIS considers both the distance to an ideal best
solution, , and the distance to an ideal worst solution, ,
by taking the proximity relative to the positive ideal solution.
Based on a comparison of the relative distance, an
alternative priority arrangement, , can be achieved. This
method is used to solve practical decision-making problems.
Because the concept is simple and easy to understand,
computing is efficient and has the ability to measure the
relative performance of decision alternatives. The steps for
TOPSIS are seen below.
Step 1: Vector normalization is done using equation 3.
(3)
where represents the performance values of each cell.
Step 2: Multiply the corresponding weights by the
normalized values to obtain the weighted normalized
decision values, .
Step 3: Determine the values for the ideal best and ideal
worst for each criterion. For non-beneficial factors, lower
values are the ideal best, while the higher values are the ideal
worst. For beneficial factors, the higher values are the ideal
best, while the lower values are the ideal worst. indicates
the ideal best solution. indicates the ideal worst solution.
Step 4: Calculate the Euclidean distance from the ideal best
() and the ideal worst (). They are expressed in
equations 4 and 5.
(4)
(5)
Step 5: Calculate the performance score, . It is expressed
as seen in equation 6.
(6)
The performance score is between 0 and 1. The closer it is
to 1, the more optimal the solution is.
Step 6: Rank the alternatives.
IV. RESULTS AND DISCUSSION
This research uses AHP and TOPSIS to select the
appropriate software among three proposed school
management software (SMS) based on the following factors.
Component: The software is expected to handle
admissions, fees, class assessment, academic
activities and relationship management for the
school.
Cost: This factor is to be considered to fit in into
the estimated budget of the school without
negatively affecting the turnover. It includes the
initial cost, running cost and maintenance cost.
Ease of use: The software is expected to be user-
friendly, easy to navigate, have an attractive GUI,
and have access to support.
Maturity: Software should be known for its
capability to handle school management activities.
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181http://www.ijert.org
IJERTV11IS060347 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
www.ijert.org
Vol. 11 Issue 06, June-2022
649
Fig. 1. Hierarchy Structure for the Software Selection
The above factors are arranged into a hierarchical tree to
determine their weights using AHP as shown in figure 1.
Five members of the management team made the decision
using the scale of comparison shown in Table 1. The
judgement matrix is shown.
Six comparisons were done with a consistency ratio of 4.6%.
The resultant weights of the process can be seen in Table II.
Table II. AHP Weights for Selection Factors
Criteria
Priority
Rank
Cost
0.262
2
Component
0.565
1
Ease of Use
0.118
3
Maturity
0.055
4
Based on the outcome of the AHP analysis, it can be seen
that the Component criterion has the highest threshold, with
a weight of 0.565. This implies that Component factor is
most important when considering the selection of a school
management software. The obtained weights are further
applied to select the most suitable software for the school.
The judgement is consistent because the value of the
consistency ratio is less than 10%. Fig. 2 shows the graph of
the criteria weights.
Fig. 2. Weights of Selection Criteria
Furthermore, TOPSIS is applied to rank the three SMS
alternatives. The resultant matrices and ranking are shown
in Tables III, IV and V.
TABLE III. TOPSIS Weighted Normalized Decision Matrix
COST
COMPONENT
EASE
MATURITY
SMS A
0.250412
0.374180391
0.06071
0.044610891
SMS B
0.071546
0.299344313
0.080947
0.026766534
SMS C
0.028619
0.299344313
0.06071
0.017844356
TABLE IV. Ideal Best and Ideal Worst Matrix
Ideal Best
0.028619
0.374180391
0.080947
0.044610891
Ideal Worst
0.250412
0.299344313
0.06071
0.017844356
TABLE V. Summary of Final TOPSIS Analysis
Options
Rank
SMS A
0.222715
0.079479
0.302194
0.263006
3
SMS B
0.0881
0.180228
0.268328
0.67167
2
SMS C
0.082015
0.221793
0.303808
0.730044
1
SMS Software
Selection
Component
Maturity
Ease of Use
Cost
SMS C
SMS B
SMS A
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181http://www.ijert.org
IJERTV11IS060347 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
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As shown in Table V above, the best choice is SMS C with
a score of 0.730044 because of its closeness to 1. The closer the performance score is to 1, the more excellent the
outcome is.
Fig. 3. TOPSIS Performance Score for the SMS
V. CONCLUSION
Based on the result of this study, it can be seen that software
selection process should not be handled casually but should
be done using suitable MCDM methods such as AHP and
TOPSIS. The component factor is of utmost importance
when making decision for an ideal school management
software. The weights for the criteria were obtained using
AHP while the actual ranking of the software was done
through TOPSIS. Therefore, the hybrid approach yields an
excellent result with performance score of 0.730044.
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ISSN: 2278-0181http://www.ijert.org
IJERTV11IS060347 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
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