CITSM 20171 (FIXED) Decision support systems design on sharia financing using Yager's fuzzy decision model
Decision Support Systems Design on Sharia
Financing using Yager’s Fuzzy Decision Model
¹Aries Susanto, ²Lisa Latifah, 3Nuryasin, 4Aida Fitriyani
1,2,3
Department of Information Systems
Syarif Hidayatullah State Islamic University Jakarta, Tangerang Selatan, Indonesia
4
Department of Informatics Engineering
Bhayangkara University, Jakarta, Indonesia
Abstract- Financing based on Islamic banking is one of our most
desirable communities. In analyzing customer financing filing
takes a relatively long time because the assessment process is
carried out based on the subjectivity of the leaders and decision
makers no accurate calculations in determining the customer is
acceptable or not in the filing of financing. Absence of Decision
Support Systems to assist in calculating eligibility chief
marketing customer financing led to the assessment process lasts
longer and inaccurate. The purpose of this study to help the
performance of the head of marketing in giving a decision based
on the assessment criteria that apply are using Decision Support
System. The Decision Support System using Fuzzy MADM with
Yager model and assessment criteria 5C (Character, Capacity,
Capital, Collateral and Condition), system development methods
Rapid Application Development (RAD) and Unified Modeling
Language (UML) as tools of system design. Decision Support
System is expected to help the performance of the head of
marketing in making a decision on the filing of financing
customers quickly and accurately.
Keywords- Islamic banking; fuzzy MADM; Yager model;
information systems; decision support systems; problem solving;
finance
I. INTRODUCTION
Nowadays, technology development has a lot to offer for
people like convenience who run activities [6]. Profit-based
organizations (companies) as well as non-profit organizations
(including government agencies) feel many benefits of
technological advances [3]. Presence technology can aim to
achieve better results with the level of effectiveness and high
accuracy.
Similarly, in the decision-making process in the shariabased banking treasure activities, information is needed in this
era of globalization because all the parties are required to
move quickly and precisely in taking a decision and action.
Such business activities that is collecting funds from public
in the form of deposits such as time deposits, savings or other
forms and distributing the funds in the form of loans [5]. One
of the funds distributed in the form of loans to customers using
the Murabaha-based contract [13].
Customers who want to do the financing treasure first
analyzed whether the customer is feasible or not get a loan
financing in accordance with the applicable criteria in treasure
starting from the marketing to select files and analyze data
customers to do the process of scoring by head of marketing
financing.
The process of granting scoring to have an appropriate
decision in the sharia-based banking still subjective nature so
that the decision makers often have difficulty in processing
customer data to perform calculations feasibility assessment
customers. That is why it needs a system that can generate
data assessment ranking system which prospective customers
financing most recommended to facilitate the performance of
the head of marketing in its assessment of Murabaha-based
finance to prospective customers. With this assessment, the
expected head of marketing can work quickly and of course,
customers do not have to wait long processing time if the
admissions process whether or not a loan fund for designated
financing.
Based on the issues described above, the need for Decision
Support System to select prospective customers filing
Murabaha-based financing for the results of the assessment
carried out more quickly and precisely so that it can help the
performance of the management of a sharia-based banking
treasure.
Therefore, this study proposed a review on Decision
Support System based on Murabaha method using Fuzzy
MADM (Multi Attribute Decision Making)-based Yager
model which is expected to help provide solutions to the
assessment of prospective customers filing financing to
management in future.
II. THEORETICAL BACKGROUND
A.
Basic Concepts of Information Systems
The system is a network of some procedures that are
interconnected, gathered together to perform an activity or to
accomplish a specific goal [8]. Moreover, useful information
as data that has been processed into a form that is more
meaningful and useful for the recipient can use to take a
decision in the present and in the future and has the
characteristics of true or false, a new, additional and corrective
[8]. The information system can be defined as a system
created by humans which consists of components within the
organization to achieve a goal that is present information [1].
Therefore, a set of organizational procedures when
implemented will provide information for decision makers and
or to control the organization.
B. Decision Support Systems (DSS)
Decision Support System is an information system that can
help identify opportunities decisions or provide information to
assist in making a decision [9]. It has a specific objectives and
principles such as assisting managers in making decisions on
semi-structured problems, providing support for consideration
of the manager, and improving the effectiveness of the
decisions a manager [3].
Moreover, Decision Support System is also a system
designed to assist management in the process of making
decisions and to improve the process and quality of the
decision-making results. The system is not intended to replace
the role of the decision makers, but the system is made to
produce alternative offered decision-makers in their duties [3].
Decision Support System needs an architecture to make a
decision is appropriately taken and can give a best alternate
for a needed decision by managers. It may consist of four
components or subsystems are [12] such as:
1. Data Management Subsystem
Data management subsystems which include a database
containing the relevant data for a situation that is set by a
software called the Database Management System (DBMS).
2. Model Management Subsystem
A software package which include financial models,
statistics, and management science or various other
quantitative models that can provide analytic and
management capabilities for the right software. This
software is often called the Model Base Management
System (MBMS).
3. User Interface Subsystem
This subsystem allows the user to be able to communicate
and give orders to the SPK. These subsystems are managed
by a User Interface Management System (UIMS).
4. Knowledge-Based Management Subsystem
These subsystems can provide some of the skills necessary
to solve a problem and provide a knowledge that can
improve the operation of the components of the other SPK.
These components form a Decision Support System that can
be connected to the internet.
C. Fuzzy MADM (Multi Attribute Decision Making) Method
Fuzzy (MADM) is a method used to find the optimal
alternative of a number of alternatives to certain criteria [14].
The essence of Fuzzy MADM is to determine the weight
values for each attribute, followed by ranking process that will
select the alternative that has been given. On the other hand,
concepts of Yager’s fuzzy decision model is frequently used in
Fuzzy MADM-based calculation [10].
This is a standard form of Fuzzy MADM. Let A=
{a1……an
} is the set of alternatives and presented
with a fuzzy set of attributes (cj ), j=1 ,……, m. Weights
which shows the level of importance of attributes denoted by j (wj) The value of alternative performance ai with attributes
(cj) is expressed by the degree of membership mc(xi). The final
decision is taken by the intersection of all the attributes of
fuzzy. Optimal alternative is defined such that those
alternatives contributed the highest degree of membership in
vector D [10] [14].
Fig. 1. Fuzzy-based ranking decision function
D. Yager’s Fuzzy Model Solution Procedures
There are several steps to select a decision using Yager’s
model as follows [14]:
Make a comparison matrix.
Normalized to determine the weight vector.
Calculating the value of lambda max, CI and CR.
Convert the quality criteria in the value of crisp.
Calculate and create value matrix C by means of crisp
values constitute the vector row.
Specifiy the minimum value of each attribute (vector D).
Determine the greatest value of the vector D as a result.
E. Yager’s Fuzzy Model Considerations
Beside Yager model has several strengths which may result an
appropriate decision for managers such as to make a rating on
each alternative based on the aggregation degree of
compatibility on all the criteria, to create a shape of
mathematically simple and easily understood by decisionmakers, and to assert from the objective and subjective
aspects, Yager has also some weaknesses such as not efficient
enough to solve the problem of making decisions which data
is still imprecise, uncertain and unclear. It is also assumed that
a final decision on the alternatives expressed with real
numbers, so the ranking stage might be less surrogate on some
specific issues and selection phase has focused on the problem
of aggregation [14].
III. RESEARCH METHODOLOGY
Fuzzy MADM Yager models chosen as a model of analysis
in this study because classical MADM methods such as
analytic hierarchy process (AHP), elimination and choice
expressing reality (ELECTRE), simple additive weighting
(SAW), and technique for order preference by similarity to
ideal solution (TOPSIS) [14]. We expected that this model
will derive the best criteria for making an appropriate
decision.
Furthermore, we used Rapid Application Development
(RAD) method for our system development model where
RAD is an object-based approach using UML (Unified
Modeling Language) as a modeling convention set used to
define or describe a software system which is associated with
the object [1][9] to the system development that includes a
method development as well as software tools [11]. UML also
provides several visual diagram that shows various aspects of
the system [1].
A. Data Collection
We used types of method on data collection such as
observation at a Rural Sharia Bank in Jakarta, interview with
persons in charge at Murabaha-based financing department,
and literature review of Information Systems and DSS.
Total Criteria (n)
CI calculation:
IV. RESULTS AND DISCUSSION
By following several steps as discussed above to select a
decision, we have several calculation using Fuzzy MADM
Yager as follows:
CR calculation:
TABLE I
= 0,043
COMPARISON MATRIX
Character
Capacity
Capital
Condition
Collateral
Character
1
3
3
4
7
Capacity
0,333
1
2
3
5
Capital
0,333
0,5
1
3
3
Condition
0,25
0,333
0,333
1
3
Collateral
0,143
0,2
0,333
0,333
1
Total
2,058
5,033
6,666
11,333
19
The next step to do is converting the value criteria into crisp
values intended for the system to process the understandable
values by the system, especially in the decision-making
method that uses a Fuzzy MADM. The result after conversion
process is shown on the below table:
TABLE II
NORMALIZATION
Character
Cr
0,486
Ca
0,596
Cp
0,45
Co
0,353
Cl
0,368
Total
2,253
Vector
Avg.
0,451
Capacity
0,162
0,199
0,3
0,265
0,263
1,189
0,238
Capital
0,162
0,099
0,15
0,265
0,158
0,834
0,167
Condition
0,121
0,066
0,05
0,088
0,158
0,483
0,097
Collateral
0,069
0,04
0,05
0,029
0,053
0,241
0,048
TABLE III
TABLE V
QUALITY VALUE OF CRITERIA
Customer
Character
Capacity
Capital
Condition
Collateral
A
Nasabah
Good
Good
Average
Average
B
Average
C
Very
Good
Baik
Average
Very
Good
Good
Very
Good
Average
Very
Good
Good
Bad
Good
Good
Baik
Very
Good
Good
Good
D
The values are then converted into determined weight crisp
values and will give the following values:
CALCULATION MATRIX ON EACH LINE
Character
Average
TABLE V
Cr
Ca
Cp
Co
Cl
Total
0,451
0,714
0,501
0,388
0,336
2,39
Customer
Character
Capacity
Capital
Condition
Collateral
0.8
0,8
0,6
1
0,6
CRISP VALUE CONVERSION
Capacity
0,150
0,238
0,334
0,291
0,24
1,253
Capital
0,150
0,119
0,167
0,291
0,144
0,871
A
Nasabah
B
0,6
1
1
0,8
0,6
1
0,8
0,6
0,4
0,8
0,6
0,8
1
0,8
0,8
Condition
0,112
0,079
0,055
0,097
0,144
0,487
C
Collateral
0,064
0,047
0,055
0,032
0,048
0,246
D
Note: Cr: Character; Cl: Capital; Ca: Capacity; Cp: Capital; Co: Condition;
Cl: Collateral
TABLE IV
AVERAGE VECTOR DIVISION
Matrix C is then formulated to find the highest to the lowest
value of each criterion and prospective customer. The
calculation of the value of C by constituting crisp values of the
weight vectors shown in the below table of normalization.
Total
Vector Avg.
Outcome
Character
2,39
0,451
5,299
Capital
1,253
0,238
5,264
Capacity
0,871
0,167
5,215
Nilai C
Lisa
Latifah
Fachry
Suny
Condition
0,487
0,097
5,020
CI(
0,904
0,794
1
0,794
C2(
0,948
1
0,948
0,948
C3(
0,918
1
0,918
1
C4(
1
0,978
0,914
0,978
C5(
0,975
0,975
0,989
0,989
Collateral
0,46
0,048
5,583
By using the outcomes from above tables, we may then
calculate with Yager’s formula to get total criteria.
TABLE V
CRISP VALUE CALCULATION
Finally we can have optimal alternative through the highest
degree of membership in vector D by transposing matrix C
results as follows:
TABLE VI
MINIMUM VALUE OF ATTRIBUTES
REFERENCES
[1]
[2]
[3]
[4]
[5]
V. CONCLUSIONS
Vector D value is the result of an assessment using Fuzzy
MADM Yager model to determine the most prospective
customers with better eligibility through the highest given. By
using vectors value D which have been calculated from the
sum of each value of the criteria {0.904; 0.794; 0.914; 0.794},
the most prospective customer is customer C (D3) with a
value 0.914. He/she is chosen as the most prospective
customer to be given Murabaha financing.
Other considerations are found here is firstly, the system is
simply designed to assist the head of marketing in making
decisions by using fuzzy MADM Model Yager where with
this method every criteria given weight value in accordance
with priorities to be more desired. The system was built
streamline the decision-making process time to be more
efficient.
Secondly, Decision Support System was built to produce the
assessment calculation based on subjective and objective
assessment criteria for using 5C: Character, Capacity, Capital,
Collateral and Condition.
The system can present an assessment based on a
calculation of the value of each criteria such as excellent with
a value of 1, 0.8, 0.6, 0.4 and 0.2 to raise to the results of the
weight vector obtained from the input value of the comparison
criteria resulting in a sequence of customer data that is most
recommended.
VI. LIMITATIONS AND FUTURE STUDIES
The system may be developed by adding other assessment
criteria, not only using the criteria 5C (Character, Capacity,
Condition, Capital, Collateral), but by adding a 7P principles
such as Personality, Purpose, Prospect, Payment, Profitability,
Protection and Party.
Future studies are expected not only to the stage of the
assessment process if the customer is accepted or not get a
loan financing, but until the process of the client mortgage
payments for each month process.
ACKNOWLEDGMENT
We would like to thank A’ang Subiyakto for his comments
and recommendations.
[6]
[7]
[8]
[9]
[10]
[11]
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(Sistem Informasi Konsep dan Aplikasi). Yogyakarta: Pustaka Pelajar,
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A. Susanto, H. Lee, H. Zo, A.P. Ciganek, "User acceptance of Internet
banking in Indonesia: initial trust formation," Information Development,
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use the smartphone banking services: an extension to the expectationconfirmation model," Industrial Management & Data Systems, vol. 3,
pp.508-525, April 2016.
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adopting Internet banking in Indonesia. In International Conference on
Advanced Computer Science and Information System, pp. 305-310,
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Intelligent Systems (Sistem Pendukung Keputusan dan Sistem
Kecerdasan), Yogyakarta: Penerbit Andi, 2005.
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Sistem Informasi), Yogyakarta: Penerbit Andi, 2005.
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Control. London: World Scientific, 2002.
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Perancangan dan Pengelolaan Basis Data). Yogyakarta: Andi, 2007.
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(Bank Syariah dari Teori ke Praktik). Jakarta: Gema Insani, 2009.
S. Kusumadewi, S. Hartati, A. Harjoko, and R. Wardoyo. Fuzzy MultiAttribute Decision Making (Fuzzy MADM). Yogyakarta: Graha Ilmu,
2006.
Financing using Yager’s Fuzzy Decision Model
¹Aries Susanto, ²Lisa Latifah, 3Nuryasin, 4Aida Fitriyani
1,2,3
Department of Information Systems
Syarif Hidayatullah State Islamic University Jakarta, Tangerang Selatan, Indonesia
4
Department of Informatics Engineering
Bhayangkara University, Jakarta, Indonesia
Abstract- Financing based on Islamic banking is one of our most
desirable communities. In analyzing customer financing filing
takes a relatively long time because the assessment process is
carried out based on the subjectivity of the leaders and decision
makers no accurate calculations in determining the customer is
acceptable or not in the filing of financing. Absence of Decision
Support Systems to assist in calculating eligibility chief
marketing customer financing led to the assessment process lasts
longer and inaccurate. The purpose of this study to help the
performance of the head of marketing in giving a decision based
on the assessment criteria that apply are using Decision Support
System. The Decision Support System using Fuzzy MADM with
Yager model and assessment criteria 5C (Character, Capacity,
Capital, Collateral and Condition), system development methods
Rapid Application Development (RAD) and Unified Modeling
Language (UML) as tools of system design. Decision Support
System is expected to help the performance of the head of
marketing in making a decision on the filing of financing
customers quickly and accurately.
Keywords- Islamic banking; fuzzy MADM; Yager model;
information systems; decision support systems; problem solving;
finance
I. INTRODUCTION
Nowadays, technology development has a lot to offer for
people like convenience who run activities [6]. Profit-based
organizations (companies) as well as non-profit organizations
(including government agencies) feel many benefits of
technological advances [3]. Presence technology can aim to
achieve better results with the level of effectiveness and high
accuracy.
Similarly, in the decision-making process in the shariabased banking treasure activities, information is needed in this
era of globalization because all the parties are required to
move quickly and precisely in taking a decision and action.
Such business activities that is collecting funds from public
in the form of deposits such as time deposits, savings or other
forms and distributing the funds in the form of loans [5]. One
of the funds distributed in the form of loans to customers using
the Murabaha-based contract [13].
Customers who want to do the financing treasure first
analyzed whether the customer is feasible or not get a loan
financing in accordance with the applicable criteria in treasure
starting from the marketing to select files and analyze data
customers to do the process of scoring by head of marketing
financing.
The process of granting scoring to have an appropriate
decision in the sharia-based banking still subjective nature so
that the decision makers often have difficulty in processing
customer data to perform calculations feasibility assessment
customers. That is why it needs a system that can generate
data assessment ranking system which prospective customers
financing most recommended to facilitate the performance of
the head of marketing in its assessment of Murabaha-based
finance to prospective customers. With this assessment, the
expected head of marketing can work quickly and of course,
customers do not have to wait long processing time if the
admissions process whether or not a loan fund for designated
financing.
Based on the issues described above, the need for Decision
Support System to select prospective customers filing
Murabaha-based financing for the results of the assessment
carried out more quickly and precisely so that it can help the
performance of the management of a sharia-based banking
treasure.
Therefore, this study proposed a review on Decision
Support System based on Murabaha method using Fuzzy
MADM (Multi Attribute Decision Making)-based Yager
model which is expected to help provide solutions to the
assessment of prospective customers filing financing to
management in future.
II. THEORETICAL BACKGROUND
A.
Basic Concepts of Information Systems
The system is a network of some procedures that are
interconnected, gathered together to perform an activity or to
accomplish a specific goal [8]. Moreover, useful information
as data that has been processed into a form that is more
meaningful and useful for the recipient can use to take a
decision in the present and in the future and has the
characteristics of true or false, a new, additional and corrective
[8]. The information system can be defined as a system
created by humans which consists of components within the
organization to achieve a goal that is present information [1].
Therefore, a set of organizational procedures when
implemented will provide information for decision makers and
or to control the organization.
B. Decision Support Systems (DSS)
Decision Support System is an information system that can
help identify opportunities decisions or provide information to
assist in making a decision [9]. It has a specific objectives and
principles such as assisting managers in making decisions on
semi-structured problems, providing support for consideration
of the manager, and improving the effectiveness of the
decisions a manager [3].
Moreover, Decision Support System is also a system
designed to assist management in the process of making
decisions and to improve the process and quality of the
decision-making results. The system is not intended to replace
the role of the decision makers, but the system is made to
produce alternative offered decision-makers in their duties [3].
Decision Support System needs an architecture to make a
decision is appropriately taken and can give a best alternate
for a needed decision by managers. It may consist of four
components or subsystems are [12] such as:
1. Data Management Subsystem
Data management subsystems which include a database
containing the relevant data for a situation that is set by a
software called the Database Management System (DBMS).
2. Model Management Subsystem
A software package which include financial models,
statistics, and management science or various other
quantitative models that can provide analytic and
management capabilities for the right software. This
software is often called the Model Base Management
System (MBMS).
3. User Interface Subsystem
This subsystem allows the user to be able to communicate
and give orders to the SPK. These subsystems are managed
by a User Interface Management System (UIMS).
4. Knowledge-Based Management Subsystem
These subsystems can provide some of the skills necessary
to solve a problem and provide a knowledge that can
improve the operation of the components of the other SPK.
These components form a Decision Support System that can
be connected to the internet.
C. Fuzzy MADM (Multi Attribute Decision Making) Method
Fuzzy (MADM) is a method used to find the optimal
alternative of a number of alternatives to certain criteria [14].
The essence of Fuzzy MADM is to determine the weight
values for each attribute, followed by ranking process that will
select the alternative that has been given. On the other hand,
concepts of Yager’s fuzzy decision model is frequently used in
Fuzzy MADM-based calculation [10].
This is a standard form of Fuzzy MADM. Let A=
{a1……an
} is the set of alternatives and presented
with a fuzzy set of attributes (cj ), j=1 ,……, m. Weights
which shows the level of importance of attributes denoted by j (wj) The value of alternative performance ai with attributes
(cj) is expressed by the degree of membership mc(xi). The final
decision is taken by the intersection of all the attributes of
fuzzy. Optimal alternative is defined such that those
alternatives contributed the highest degree of membership in
vector D [10] [14].
Fig. 1. Fuzzy-based ranking decision function
D. Yager’s Fuzzy Model Solution Procedures
There are several steps to select a decision using Yager’s
model as follows [14]:
Make a comparison matrix.
Normalized to determine the weight vector.
Calculating the value of lambda max, CI and CR.
Convert the quality criteria in the value of crisp.
Calculate and create value matrix C by means of crisp
values constitute the vector row.
Specifiy the minimum value of each attribute (vector D).
Determine the greatest value of the vector D as a result.
E. Yager’s Fuzzy Model Considerations
Beside Yager model has several strengths which may result an
appropriate decision for managers such as to make a rating on
each alternative based on the aggregation degree of
compatibility on all the criteria, to create a shape of
mathematically simple and easily understood by decisionmakers, and to assert from the objective and subjective
aspects, Yager has also some weaknesses such as not efficient
enough to solve the problem of making decisions which data
is still imprecise, uncertain and unclear. It is also assumed that
a final decision on the alternatives expressed with real
numbers, so the ranking stage might be less surrogate on some
specific issues and selection phase has focused on the problem
of aggregation [14].
III. RESEARCH METHODOLOGY
Fuzzy MADM Yager models chosen as a model of analysis
in this study because classical MADM methods such as
analytic hierarchy process (AHP), elimination and choice
expressing reality (ELECTRE), simple additive weighting
(SAW), and technique for order preference by similarity to
ideal solution (TOPSIS) [14]. We expected that this model
will derive the best criteria for making an appropriate
decision.
Furthermore, we used Rapid Application Development
(RAD) method for our system development model where
RAD is an object-based approach using UML (Unified
Modeling Language) as a modeling convention set used to
define or describe a software system which is associated with
the object [1][9] to the system development that includes a
method development as well as software tools [11]. UML also
provides several visual diagram that shows various aspects of
the system [1].
A. Data Collection
We used types of method on data collection such as
observation at a Rural Sharia Bank in Jakarta, interview with
persons in charge at Murabaha-based financing department,
and literature review of Information Systems and DSS.
Total Criteria (n)
CI calculation:
IV. RESULTS AND DISCUSSION
By following several steps as discussed above to select a
decision, we have several calculation using Fuzzy MADM
Yager as follows:
CR calculation:
TABLE I
= 0,043
COMPARISON MATRIX
Character
Capacity
Capital
Condition
Collateral
Character
1
3
3
4
7
Capacity
0,333
1
2
3
5
Capital
0,333
0,5
1
3
3
Condition
0,25
0,333
0,333
1
3
Collateral
0,143
0,2
0,333
0,333
1
Total
2,058
5,033
6,666
11,333
19
The next step to do is converting the value criteria into crisp
values intended for the system to process the understandable
values by the system, especially in the decision-making
method that uses a Fuzzy MADM. The result after conversion
process is shown on the below table:
TABLE II
NORMALIZATION
Character
Cr
0,486
Ca
0,596
Cp
0,45
Co
0,353
Cl
0,368
Total
2,253
Vector
Avg.
0,451
Capacity
0,162
0,199
0,3
0,265
0,263
1,189
0,238
Capital
0,162
0,099
0,15
0,265
0,158
0,834
0,167
Condition
0,121
0,066
0,05
0,088
0,158
0,483
0,097
Collateral
0,069
0,04
0,05
0,029
0,053
0,241
0,048
TABLE III
TABLE V
QUALITY VALUE OF CRITERIA
Customer
Character
Capacity
Capital
Condition
Collateral
A
Nasabah
Good
Good
Average
Average
B
Average
C
Very
Good
Baik
Average
Very
Good
Good
Very
Good
Average
Very
Good
Good
Bad
Good
Good
Baik
Very
Good
Good
Good
D
The values are then converted into determined weight crisp
values and will give the following values:
CALCULATION MATRIX ON EACH LINE
Character
Average
TABLE V
Cr
Ca
Cp
Co
Cl
Total
0,451
0,714
0,501
0,388
0,336
2,39
Customer
Character
Capacity
Capital
Condition
Collateral
0.8
0,8
0,6
1
0,6
CRISP VALUE CONVERSION
Capacity
0,150
0,238
0,334
0,291
0,24
1,253
Capital
0,150
0,119
0,167
0,291
0,144
0,871
A
Nasabah
B
0,6
1
1
0,8
0,6
1
0,8
0,6
0,4
0,8
0,6
0,8
1
0,8
0,8
Condition
0,112
0,079
0,055
0,097
0,144
0,487
C
Collateral
0,064
0,047
0,055
0,032
0,048
0,246
D
Note: Cr: Character; Cl: Capital; Ca: Capacity; Cp: Capital; Co: Condition;
Cl: Collateral
TABLE IV
AVERAGE VECTOR DIVISION
Matrix C is then formulated to find the highest to the lowest
value of each criterion and prospective customer. The
calculation of the value of C by constituting crisp values of the
weight vectors shown in the below table of normalization.
Total
Vector Avg.
Outcome
Character
2,39
0,451
5,299
Capital
1,253
0,238
5,264
Capacity
0,871
0,167
5,215
Nilai C
Lisa
Latifah
Fachry
Suny
Condition
0,487
0,097
5,020
CI(
0,904
0,794
1
0,794
C2(
0,948
1
0,948
0,948
C3(
0,918
1
0,918
1
C4(
1
0,978
0,914
0,978
C5(
0,975
0,975
0,989
0,989
Collateral
0,46
0,048
5,583
By using the outcomes from above tables, we may then
calculate with Yager’s formula to get total criteria.
TABLE V
CRISP VALUE CALCULATION
Finally we can have optimal alternative through the highest
degree of membership in vector D by transposing matrix C
results as follows:
TABLE VI
MINIMUM VALUE OF ATTRIBUTES
REFERENCES
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[4]
[5]
V. CONCLUSIONS
Vector D value is the result of an assessment using Fuzzy
MADM Yager model to determine the most prospective
customers with better eligibility through the highest given. By
using vectors value D which have been calculated from the
sum of each value of the criteria {0.904; 0.794; 0.914; 0.794},
the most prospective customer is customer C (D3) with a
value 0.914. He/she is chosen as the most prospective
customer to be given Murabaha financing.
Other considerations are found here is firstly, the system is
simply designed to assist the head of marketing in making
decisions by using fuzzy MADM Model Yager where with
this method every criteria given weight value in accordance
with priorities to be more desired. The system was built
streamline the decision-making process time to be more
efficient.
Secondly, Decision Support System was built to produce the
assessment calculation based on subjective and objective
assessment criteria for using 5C: Character, Capacity, Capital,
Collateral and Condition.
The system can present an assessment based on a
calculation of the value of each criteria such as excellent with
a value of 1, 0.8, 0.6, 0.4 and 0.2 to raise to the results of the
weight vector obtained from the input value of the comparison
criteria resulting in a sequence of customer data that is most
recommended.
VI. LIMITATIONS AND FUTURE STUDIES
The system may be developed by adding other assessment
criteria, not only using the criteria 5C (Character, Capacity,
Condition, Capital, Collateral), but by adding a 7P principles
such as Personality, Purpose, Prospect, Payment, Profitability,
Protection and Party.
Future studies are expected not only to the stage of the
assessment process if the customer is accepted or not get a
loan financing, but until the process of the client mortgage
payments for each month process.
ACKNOWLEDGMENT
We would like to thank A’ang Subiyakto for his comments
and recommendations.
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