It can be affirmed that all the pro

It can be affirmed that all the proposed fuzzy risk assessment
methods have a common procedure (Lyons and Skitmore,
2004):
1. Definition and measurement of parameters: The fundamental
parameters, by which the risks associated with
a project are assessed, are risk probability and risk
severity, although other parameters can be defined.
The measurement of these parameters frequently is difficult
due to the great uncertainty involved. In these
cases, the measurement of each parameter is made in
vague data or linguistic terms and converted into its corresponding
fuzzy number.
2. Definition of fuzzy inference: The relations between input
parameters and output parameters can be defined in form
of “if-then” rules or in form of mathematical function
defined by an appropriated fuzzy arithmetic operator.
3. Defuzzification: As the result of a fuzzy inference phase
is a fuzzy number, this step is used to convert the fuzzy
result into a exact numerical value that can adequately
represent it. In some risk assessment methodologies, some judgements
are done by means of pair-wise comparisons (Zeng
et al., 2007; Wang and Elhag, 2007; Zhang and Zou,
2007). Preference information of alternatives generally presents
inconsistency problems. Although there are a large
number of studies about the inconsistency of fuzzy preference
relations (Dong et al., 2008; Ghazanfari and Nojavan,
2004); (Herrera-Viedma et al., 2004; Ma et al., 2006; Wang
and Chen, 2008), none of the proposed fuzzy risk assessment
methodologies take into account the inconsistency
of the judgements. This paper presents a fuzzy risk assessment
model which most significant difference with other
fuzzy risk assessment methods is the use of an algorithm
to handle the inconsistencies in the fuzzy preference relation
when pair-wise comparison judgements are necessary.

Results (Indonesian) 1:

It can be affirmed that all the proposed fuzzy risk assessmentmethods have a common procedure (Lyons and Skitmore,2004):1. Definition and measurement of parameters: The fundamentalparameters, by which the risks associated witha project are assessed, are risk probability and riskseverity, although other parameters can be defined.The measurement of these parameters frequently is difficultdue to the great uncertainty involved. In thesecases, the measurement of each parameter is made invague data or linguistic terms and converted into its correspondingfuzzy number.2. Definition of fuzzy inference: The relations between inputparameters and output parameters can be defined in formof “if-then” rules or in form of mathematical functiondefined by an appropriated fuzzy arithmetic operator.3. Defuzzification: As the result of a fuzzy inference phaseis a fuzzy number, this step is used to convert the fuzzyresult into a exact numerical value that can adequatelyrepresent it. In some risk assessment methodologies, some judgementsare done by means of pair-wise comparisons (Zenget al., 2007; Wang and Elhag, 2007; Zhang and Zou,2007). Preference information of alternatives generally presentsinconsistency problems. Although there are a largenumber of studies about the inconsistency of fuzzy preferencerelations (Dong et al., 2008; Ghazanfari and Nojavan,2004); (Herrera-Viedma et al., 2004; Ma et al., 2006; Wangdan Chen, 2008), tidak ada yang diusulkan kabur risiko penilaianmetodologi memperhitungkan inkonsistensipenilaian. Makalah ini menyajikan penilaian risiko kaburmodel perbedaan yang paling signifikan lainmetode penilaian risiko kabur adalah penggunaan algoritmauntuk menangani ketidakkonsistenan dalam hubungan kabur preferensiKetika pair-wise perbandingan penilaian diperlukan.

Results (Indonesian) 2:

Hal ini dapat ditegaskan bahwa semua penilaian risiko kabur yang diusulkan
metode memiliki prosedur umum (Lyons dan Skitmore,
2004):
1. Definisi dan pengukuran parameter: The mendasar
parameter, dimana risiko yang terkait dengan
proyek yang dinilai, yang kemungkinan risiko dan risiko
keparahan, meskipun parameter lainnya dapat didefinisikan.
Pengukuran parameter ini sering sulit
karena ketidakpastian besar yang terlibat. Dalam
kasus, pengukuran setiap parameter dibuat dalam
data yang tidak jelas atau istilah linguistik dan diubah menjadi yang sesuai
nomor fuzzy.
2. Definisi inferensi fuzzy: Hubungan antara input
parameter dan output parameter dapat didefinisikan dalam bentuk
"jika-maka" aturan atau dalam bentuk fungsi matematika
didefinisikan oleh disesuaikan Operator aritmatika fuzzy.
3. Defuzzifikasi: Sebagai hasil dari fase inferensi fuzzy
adalah angka fuzzy, langkah ini digunakan untuk mengkonversi kabur
hasil menjadi nilai numerik yang tepat yang memadai dapat
mewakilinya. Dalam beberapa metodologi penilaian risiko, beberapa penilaian
yang dilakukan dengan cara perbandingan berpasangan (Zeng
et al, 2007;. Wang dan Elhag, 2007; Zhang dan Zou,
2007). Informasi preferensi alternatif umumnya menyajikan
masalah inkonsistensi. Meskipun ada yang besar
sejumlah studi tentang inkonsistensi preferensi kabur
hubungan (Dong et al, 2008;. Ghazanfari dan Nojavan,
2004); (Herrera-Viedma et al, 2004;. Ma et al, 2006;. Wang
dan Chen, 2008), tidak ada penilaian risiko kabur yang diusulkan
metodologi memperhitungkan inkonsistensi
dari penilaian. Makalah ini menyajikan penilaian risiko kabur
Model yang Perbedaan yang paling signifikan dengan lainnya
metode penilaian risiko kabur adalah penggunaan algoritma
untuk menangani inkonsistensi dalam preferensi kaitannya kabur
saat berpasangan penilaian perbandingan yang diperlukan.