Zadeh, 1978. Fuzzy logic is a multi-valued theory where in intermediate values such as “moderate”, “high”, “low” were used instead of yes or no, true or false as
it is used in the conventional crisp theory. The fuzzy sets are defined by the embership functions. The fuzzy sets represent the grade of any element x of X
hav embership to A. The degree to which an element belongs to
a set is
l fuzzy sets representing linguistic concepts such as low, medium, high, a
overcome the inability of AHP to handle imprecision and subjective- ness in
r’s uncerta
m that
e the partial m defined by the value between 0 and 1.
If an element x really belongs to A if Ax = 1, and x not belongs to A if Ax = 0. The higher is the membership value, the greater is the element x belong
to a set A. Severa
nd so on are often employed to define states of a variable. Such a variable is usually called a fuzzy variable. The significance of fuzzy variables is that they
facilitate gradual transitions between states and, consequently, possess a natural capability to express and deal with observation and measurement vagueness.
2.9 Fuzzy AHP
To pairwise comparison process has been extended Saaty’s AHP by Buckley
and Laarhoven and Pedricz Deng, 1999. Jeganathan 2003 has modified the methodology that that create simple, improved, and sophisticated approach using
fuzzy logic. Fuzzy AHP use a range of value to incorporate decision make
inty. From this range decision maker can select the values that reflect their confidence and also can specify the attitude like optimistic, pessimistic or
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moderate Jeganathan 2003 in Kuswandari 2004. Optimistic attitude is represented by the highest value range, moderate attitude is represented by the
middle value of range and pessimistic attitude is represented by the lowest value of range.
Triangular or trapezoidal fuzzy number is used to express the decision ith respect to each criterion. After the criteria
are wei
ept of fuzzy extent analysis is applied to solve the fuzzy recipro
the problems that involved qualitative information. To avoid complex maker’s assessment on alternatives w
ghted, the overall utilities of alternatives represented by fuzzy number are aggregated by fuzzy arithmetic using Simple Additive Weighting method. To
prioritize the alternatives, their fuzzy utilities need to be compared and ranked. The conc
cal matrix for determining the criteria importance and alternative performance. To avoid the complex and unreliable process of comparing fuzzy
utilities, the alpha-cut concept is used to transform the fuzzy performance matrix representing the overall performance of all alternatives with respect to each
criterion into an interval performance matrix.
2.10 Previous Related-Research
The illustration a method for constructing an integrated system of GIS, multi-criteria decision method and expert systems applies by Jun 2000 to an
industrial site selection problem that searches for manufacturing facilities sites in a regional scale.
Deng 1999 proposed a new method for Multi-Criteria Analysis that required no complex calculations. This method could be applied effectively for
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comparison of fuzzy utilities, Deng introduced α-cut analysis. The method is well
designed to deal with all kind of vagueness. α-cut analysis made possible to
incorpo
M and Ordere
rate the ambiguity in expert knowledge and using the optimism index λ
to address the decision makers attitude. Prakash 2003 applied multi-criteria decision making technique using
Fuzzy Logic in land suitability analysis for agricultural crop. This study compared three approaches for land suitability, AHP, Ideal Vector Approach and
Fuzzy AHP. The process using Fuzzy AHP incorporated the AHP, fuzzy numbers, fuzzy extent analysis, alpha cut and lambda function. The ability of the
three techniques to model the sensitivity of decision making process is also investigated. This study found that Fuzzy AHP performs better than the other two
approaches. The study about hazard assessment and vulnerable analysis has been
performed by some researchers. Yalcin, et al 2002 integrated GIS and Multi-
criteria Decision Analysis MCDA to generate a composite map for decision makers by using some effective factors causing flood. The analysis compared
Boolean Approach, Ranking Method, Pairwise Comparison Method PC d Weighted Averaging OWA Method including fuzzy concept on
standardization of the criterion values. The study stated that using fuzzy logic reduced the error due to the standardization and classification of the value, but the
application of fuzzy measures in Multi-criteria Evaluation in general and OWA in particular require further research.
Rashed and John Weeks 2003 present a methodology to assess urban vulnerability to earthquakes into a GIS framework that combines elements from
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the techniques of spatial multi-criteria analysis and fuzzy logic. The results suggest that the proposed methodology may provide a new approach for analysing
vulnerability that can add to understanding of humanhazards interaction. As consequently of mud volcano in Sidoarjo, numerous identification and
analysis about impacts of the mud flow was conducted. Awad 2006 gives a qualitative analysis of the types of risk associated with the discharge of the mud to
the sea, and the marine resources under threat. It also analyses three possible scenarios for mud discharge to the sea, outlining the potential repercussions. Pohl
2007 present some identification and analysis about the efforts to stop the mud flows, some impacts caused by the continuing unstopped mudflow, cost and
compensation payment for victims and demands to the responsible companies and the Indonesian government. UN 2006 provides technical assistance to the
environmental authorities with the identification of environmental impacts of the mud, and based on the outcomes, provide recommendations for mitigation. The
other survey and research based on geological characteristic also conducted by centre of environmental geology department Pusat Lingkungan Geologi, 2007
and Rumbudi, 2007.
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III. METHODOLOGY
3.1 Time and Location
This research was conducted from March to July 2008 including process of developing method and implementation of method. Developing the method was
accomplished at Bogor Agricultural University, while implementation of method was conducted for study area in Sidoarjo Regency, East Java Province.
3.1.1 General Overview of Study Area
The study area is working area of The National Sidoarjo Mudflow Mitigation Team Badan Penanggulangan Lumpur Lapindo or BPLS, which is
located in Sidoarjo Regency, Province of East Java covering three districts i.e. Porong, Jabon and Tanggulangin. The location is shown in Figure 3.1.
Figure 3.1 The study areas in Sidoarjo, East Java
