PENGEMBANGAN METODA PENGAMBILAN KEPUTUSAN MULTI KRITERIA DENGAN MENGGUNAKAN KONSEP FUZZY - Hamdani (NIM : 23498077), Dani
Abstract : In certain conditions, classical decision making method using ordinal value has some weaknesses. The utilization of ordinal value can not unify individual preferences becoming a group preference. Sharma mentioned that utilization of ordinal value can not state by how much the subject prefers one alternative over other alternative16. Liao stated that the human beings are always more comfortable making imprecise verbal statements rather than quantitative estimations for expressing preference, judgment, or prediction in an uncertain environment\". By reason of that, utilization of ordinal value for decision making will effect uncertainty and ambiguity. Fuzzy set theory is very helpful in giving a solution to the uncertainty problems, either uncertainty based on unsharp boundary, perfection of information, or ambiguity. Beside that, fuzzy set theory was developed based on the premise that the key elements in human thinking are not numbers, but linguistic terms or labels of fuzzy sets\". Decision making methods using fuzzy set theory have gradually gained acceptance because of their capabilities in handling impreciseness, which commonly exists in system specifications, states, and alternatives ratings. The objective of this paper is to develop a new algorithm of multicriteria decision making using fuzzy concept, which combining the strongness of previous algorithms. Assessing is conducted to some algorithms who developed by T. Warren Liao (1996), Peri H. Iz (1994), Cheng and Chan (1994), and Tseng and Klein (1989). The methodology first ask to the decision makers, who have participated in decision making process to provide the necessary information on relevant subjects, and to decide of criteria and linguistic variables. Once obtained, the input is then automatically converted to triangular fuzzy number. Through a series of fuzzy arithmetic operations, a rating of criteria, alternative, and a group preference index are calculated for each alternative respectively. Finally, the method of ranking fuzzy numbers with dominance and indifferent concept is used to rank all of alternatives. The result of testing using hypothesis and real data present that this model is capable to differentiate alternatives, and ranks all of alternatives successfully.