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Decision Support System for Government Aid
Recipient Using SMART Method
Asep Taufik Muharram
Informatics and Computer Engineering
Politeknik Negeri Jakarta
Depok, Indonesia
asep.muharram@tik.pnj.ac.id
Iik Muhamad Malik Matin
Informatics and Computer Engineering
Politeknik Negeri Jakarta
Depok, Indonesia
iik.muhamad.malik.matin@tik.pnj.ac.id
Rizki Elisa Nalawati
Informatics and Computer Engineering
Politeknik Negeri Jakarta
Depok, Indonesia
rizkielisa@tik.pnj.ac.id
Bambang Warsuta
Informatics and Computer Engineering
Politeknik Negeri Jakarta
Depok, Indonesia
bambang.warsuta@tik.pnj.ac.id
Alifah Fadiya
Informatics and Computer Engineering
Politeknik Negeri Jakarta
Depok, Indonesia
alufah.fadiya.tik19@mhsw.pnj.ac.id
Abstract—Decision Support systems have been widely
implemented in various problem areas. This is due to the
advantages of decision support systems in providing alternative
solutions based on data and established criteria. Various
problems can be solved, including multi-criteria problems. The
problem of providing government aid is included in multi-
criteria cases. One method that can be used to solve this
problem is the use of a Simple Multi-Criteria Rating (SMART)
method. This system can provide alternatives to the problem of
providing government aid based on 14 criteria. The results of
user acceptance testing which was carried out by giving 10
questions resulted in 85.15% of the system being acceptable.
This system can provide alternatives to the problem of
providing government aid based on 14 criteria. The results of
user acceptance testing which was carried out by giving 10
questions resulted in 85.15% of the system being acceptable.
Keywords—Aid Recipient, DSS, Government, SMART
I. INTRODUCTION
The Indonesian government launched a social assistance
program for poor families in 2007. The main aim of this
assistance is to improve the quality of human resources,
especially in the fields of education and health, in poor
family groups[1]. With this assistance, poor families with
predetermined criteria will receive financial assistance for a
certain period. This system helps poor families have access
to and utilize basic social services such as health, education,
food, nutrition, care, and assistance [2]. Recipients of this
government aid must meet several predetermined criteria.
However, with a large number of aid recipients, problems in
determining aid recipients always arise, including accuracy
and speed in determining aid recipients.
Solving problems in determining families who receive
aid can be overcome by implementing a decision support
system. Decision Support Systems is a computer-based
system that supports the retrieval process of decisions in
solving problems. semi-structured problems through
alternatives obtained from processing results, data,
information, and model design [3]. The benefit of
implementing a decision support system is to improve the
ability of decision-makers by providing better decision
alternatives so that they can help make a decision[4]. The
Decision Support System (DSS), which is capable of
accepting data from users, digesting these facts, and offering
approximations of answers offered by human experts, must
be used to model human thinking and decision-making
processes [5]. An analytical tool is required to assist in the
decision-making process because the quantity of criteria and
choices in decision-making might make it difficult for
decision-makers to solve problems [6].
One method that is quite reliable in multi-criteria
problems is the use of the SMART method [7]. The term
"SMART method" stands for "Simple Multi-Attribute
Rating." Edward created the technique in 1977 as a way to
handle multi-criteria issues in a decision support system.
SMART is often widely used because of its simplicity in
responding to the needs of decision-makers, its way of
analyzing responses, and its flexible decision-making [8].
Several criteria with values are based on each alternative in
the SMART technique for multi-problem decision-making,
and each criterion is assigned a weight to compare the
relative weights of the various criteria. To choose the optimal
option, the weighted process will generate a value for each
choice [9].
In this work, we present a novel system for government
aid recipients using Simple Multi-Attribute Rating
(SMART). This method was powerful for solving multi-
criteria problems. This approach is founded on the idea that
each alternative is composed of several criteria, each of
which has a weight that indicates how significant it is about
other criteria. To select the optimal alternative, each
alternative is evaluated using the grading of these weights
[10]-[13].
The rest of the paper is organized as follows: Section II
presents related works in this area. Section III introduces the
methodology. Section IV results and discussion of system
testing of this system are discussed. Section V contains the
conclusion and future work.
II. RELATED WORKS
The Decision Support System (DSS) in the admission
system is designed to be able to provide consideration of
recipient eligibility by analyzing it based on certain criteria
that have been set in the system. Several studies on receiver
systems have been carried out [14]. The other research
discusses the development of DSS to determine scholarship
recipients using the Fuzzy Multiple Attribute Decision-
making (FMADM) method with Simple Additive Weighting
(SAW) developed the DSS model to determine recipients of
Ministry of Education scholarships by applying the SAW,
WP, and Topsis methods [15] The application of DSS to the
scholarship recipient system uses the SAW method to
determine the most deserving students to receive
scholarships and developing DSS with the Weight Product
method to determine the right choice in determining
scholarship recipients [16]. The use of the Weighted Product
(WP) method for decision support systems can also be used
to determine priority proposals for recipients of neglected
elderly social assistance [17].
III. RESEARCH METHODOLOGY
The waterfall approach was used as the research
methodology in this study. Using this approach,
departmentalization and control are made possible. phase-by-
phase model creation procedure, hence reducing potential
inaccuracies. The concept is the starting point for
development, which then progresses via design,
implementation, testing, installation, problem-solving, and
operations and maintenance [18].
A. Requirements Analysis
An essential step in developing a system or piece of
software is requirements analysis. Internal movement to
generate requirements that are uniform and clear, this stage
involves assessing, improving, and investigating the
requirements that have been gathered. The technique used for
collection and analysis, namely by conducting interviews and
observations in urban areas regarding the needs and criteria
for recipients of government aid,
B. System Design
The process of defining the architecture, interfaces, and
data for a compliant system that complies with specified
criteria is known as system design. A methodical approach
must be taken while creating and constructing systems. A
good system design must consider every aspect of
infrastructure, from hardware and software to data and the
storage of that data. Regarding the requirements analysis
stage's requirements specs, the purpose of system design is to
assist system developers in building systems that are more
effective, consistent, and scalable. Typography, color, layout
position, and coding rules are all included in this. Users can
have consistent product experiences thanks to system
designers [19].
Data is one of the elements required when utilizing the
SMART technique to develop a decision support system for
government recipients. For the process computation, these
data are presented as population assessment data as well as
criteria and sub-criteria data with their weights. The data is
processed by following the system flow shown in Fig.1.
Fig. 1. Flowchart System
C. Implementation and Unit Testing
The necessary source code is written during this stage.
The conversion of physical design standards into functional
code. The system is created in the form of little programs, or
units, which are then integrated. Unit testing is the process of
testing each unit's functioning before integration.
Implementation of the Simple Multi-Attribute Rating
Technique (SMART) method provides recommendations for
potential recipients of government aid.
D. Testing System
System testing will be explained in this chapter. After the
system implementation process is finished, system testing
will take place. System testing is done on purpose to
determine whether the constructed system can function
properly and by user requirements
IV. RESULT AND DISCUSSION
In this study, the application was designed in the form of
a website to make it easier to use. A website is a specific
address on the World Wide Web that provides certain
information and can be accessed via the Internet.
A. Determine Criteria and Weight
It is said that there are criteria utilized in this decision
support system approach, specifically based on the criteria
used as a reference in decision-making, based on papers and
data from the government. There are 14 factors used to
choose this support program, including:
• C1 = average monthly income (0.5)
• C2 = Number of family dependents (0.5)
• C3 = Status of residence (0.5)
• C4 = Ability to access education (0.5)
• C5 = Vehicle ownership (0.5)
• C6 = Type of floor (0.3)
• C7 = Wall type and condition (0.3)
• C8 = Roof type and condition (0.3)
• C9 = Source of drinking water (0.3)
• C10 = Source and installed electric power (0.3)
• C11 = Ownership and use of latrines (MCK) (0.3)
• C12 = Faecal final disposal facility (0.3)
• C13 = Have family members who are
elderly/disabled/mentally retarded/other special
needs (0.2)
• C14 = Willingness to pay for medical expenses (0.2)
B. Calculate Normalization Weight Criteria
Normalization weight criteria are calculated based on the
equation by dividing the value of each weight by the total
weight value using the normalization formula (1)
=
w
j
/ Σ
w
j
(1)
Information :
: Normalization of the weight of the jth criteria
: The weight of the jth criterion
: Total weight of all criteria
Table I contains the results of calculating the normalized
weights criteria.
TABLE I. NORMALIZATION WIGHT CRITERIA
Criteria Weight Normalization
C1 0.5 0.5/5 = 0.100
Identify the applicable funding agency here. If none, delete this text
box.
Criteria Weight Normalization
C2 0.5 0.5/5 = 0.100
C3 0.5 0.5/5 = 0.100
C4 0.5 0.5/5 = 0.100
C5 0.5 0.5/5 = 0.100
C6 0.3 0.3/5 = 0.060
C7 0.3 0.3/5 = 0.060
C8 0.3 0.3/5 = 0.060
C9 0.3 0.3/5 = 0.060
C10 0.3 0.3/5 = 0.060
C11 0.3 0.3/5 = 0.060
C12 0.3 0.3/5 = 0.060
C13 0.2 0.2/5 = 0.040
C14 0.2 0.2/5 = 0.040
Total 5 1.000
C. Determine Weight Criteria Alternative
Alternative weight criteria aim to describe the level of
priority and compare each criterion objectively by giving
relative weight to each criterion. The calculation of weight
criteria for each alternative can be seen in Table II.
TABLE II. WIGHT CRITERIA ALTERNATIVE
Criteria Alternative Value
A1 A2 A3 A4
C1 0.2 0.3 0.5 0.5
C2 0.2 0.5 0.5 0.5
C3 1 1 1 1
C4 0.1 0.5 1 1
C5 0.4 0.4 0.4 1
C6 0.5 0.8 0.8 0.9
C7 0.1 0.5 0.5 1
C8 0.4 0.4 0.6 0.8
C9 0.5 0.5 0.8 0.8
C10 0.1 0.4 0.7 1
C11 0.5 1 1 1
C12 0.8 0.8 1 1
C13 0.5 1 1 1
C14 0.1 0.5 1 1
D. Calculate Utility Value
The next step is to determine the utility value for each
criterion after entering an assessment for each one. There are
two categories of calculation methods used in utility
calculations
• Calculate Cost
Cost
u
i
(
a
i
) = (
C
max
−C
out
) / (
C
max
−C
min
) (2)
• Calculate Benefit
Benefit
u
i
(
a
i
) = (
C
out
−C
min
) / (
C
max
−C
min
) (3)
To determine aid recipient recipients using the cost formula,
the utility value must be determined (4),
i
(
i
) = (
C
max
- C
out
)/(
C
max
-C
min
) (4)
Information :
(
) : Utility value of criteria-i in alternative-i
C
: Maximum sub-criteria value
C
: Minimum sub-criteria value
C
: The value of the sub-criteria
The results of the utility value calculation can be seen in
Table III
TABLE III. UTILITY VALUE
Criteria A1 A2 A3 A4
C1 0.89 0.78 0.56 0.11
C2 0.89 0.56 0.56 0.33
C3 0.00 0.00 0.00 0.00
C4 1.00 0.56 0.00 0.00
C5 0.75 0.75 0.75 0.00
C6 0.56 0.22 0.22 0.11
C7 1.00 0.56 0.56 0.00
C8 0.75 0.75 0.50 0.25
C9 0.62 0.62 0.25 0.25
C10 1.00 0.67 0.33 0.00
C11 0.56 0.00 0.00 0.00
C12 0.25 0.25 0.00 0.00
C13 1.00 0.00 0.00 0.00
C14 1.00 0.56 0.00 0.00
E. Calculate the Final Score for Alternative
After determining the alternative utility values and the
normalized weights of each criterion, the ultimate value of
each option for a potential recipient of government help is
determined by utilizing the next step is to determine the
utility value for each criterion after entering an assessment
for each one. There are two categories of calculation
methods used in the utility formula (5).
(5)
Information :
(
) : Alternative total value
: Results of normalizing the weight of the criteria
(
) : Utility value of criteria-i in alternative-i
Table IV contains the results of calculating the final value
of each alternative.
TABLE IV. FINAL SCORE ALTERNATIVE
Alternative Final Score
A1 0.717
A2 0.470
A3 0.298
A4 0.081
F. Ranking Result
The final score is ranked based on the five categories
namely extremely poor, extremely vulnerable poor, and not
poor. The order of categories is based on the highest score to
the lowest score. Table V shows the ranking results.
TABLE V. FINAL SCORE RANKING
Alternative Final score Ranking Category
A1 0.717 1 Extremely poor
A2 0.470 2 Poor
A3 0.298 3 Vulnerable Poor
A4 0.081 4 Not Poor
G. User Acceptence Testing
An evaluation is conducted to ascertain the degree of
acceptability and support from potential system users. Using
a Likert scale of 1 to 12, measurements were taken. Potential
system users are presented with ten assertions about the
utility of the system being constructed, including points
about accessibility, points about navigation, and points about
content. Ten statements with five possible answers for each
are used to carry out the evaluation. On a Likert scale from 1
to 5, make a statement. The cost calculation approach is
employed by the author in this computation, and the criteria
5 contains the alternative ranking results [20]. The answer
choices include Strongly Disagree (SD), Disagree (D),
Neutral (N), Agree (A), and Strongly Agree (SA). Point 1
means Strongly Disagree up to point 5 which means Strongly
Agree like Table IV.
TABLE VI. USER ACCEPTANCE TESTING USING A LIKERT SCALE
No. Questions SD D N A SA
1 The use of the decision
support application is easy to
learn
2 The decision support system
service can simplify the
interaction of related sections
in my work environment
3 The use of the decision
support system can increase
my effectiveness
In doing administrative tasks
in my work environment
4 The decision support system
allows me to do
administrative tasks more
quickly in my work
environment.
5 Decision support system
allows me to more easily
process data
6 I received an authorization
model on the decision
support system for the
security of my account
7 I refuse to show my identity
when using a decision
support system
8 I will suggest using this
decision support system to
friends who are not already
using it.
9 I will still use the decision
support system service in my
work environment.
10 All the functions in the
application have met my
needs
The following formulas are used to calculate the
evaluation's findings.
• Maximum score
12 x 5 = 60
(number of respondents x highest Likert score)
• Minimum score:
12 x 1 = 12 (number of respondents x lowest Likert
score)
• Index (%) (6)
(Total Score / Maximum Score) x 100
Provide interval value on index (%)
• Index 0% - 19.99% : Strongly Disagree
• Index 20% - 39.99% : Disagree
• Index 40% - 59.99% : Neutral
• Index 60% - 79.99% : Agree
• Index 80% - 100% : Strongly Agree
Based on the results of system testing using User
Acceptance Testing (UAT), very good results were obtained
because the results of testing the Decision Support System
for Government Assistance Recipients obtained a total
percentage of 85.15%. This means that the majority of
respondents strongly agree and it is quite relevant to user
needs. Apart from that, this research shows that it is better
with a difference of 8.41 compared to the weight product
method as carried out by Surbaekti et al with a result of
76.74%.
V. CONCLUSION
The SMART method has been successfully created to
increase efficiency in the decision-making process for
recipients of government assistance. This system has passed
the testing stage. User Acceptance testing carried out on this
system reached an average percentage of 85.15% so it can be
said that the decision support system created is quite relevant
to user needs. Based on the results of research on the
decision support system for government aid recipients using
the website-based SMART method, there are still many
shortcomings. The system needs further development to
achieve satisfactory results, such as creating a system in the
form of a mobile application. Apart from this system being
able to be run using the SMART method, it is expected that
the system can be created by adding other methods as a
comparison of the results that have been obtained.
ACKNOWLEDGMENT
This research is fully funded by Politeknik Negeri
Jakarta. The authors send a sincere appreciation and thanks
to all persons who contributed to this research.
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