BibTex Citation Data :
@article{JSINBIS6213, author = {Victor Utomo and Rahmat Gernowo and Aris Sugiharto}, title = {Data-Based Fuzzy TOPSIS for Alternative Ranking}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {3}, number = {2}, year = {2013}, keywords = {}, abstract = { Technique for Order Preference by Similarity (TOPSIS) solves multi-criteria decision making (MCDM) by ranking the alternatives. When the attributes are not deterministic, a Fuzzy TOPSIS method is applied. The traditional fuzzy TOPSIS depends on decision makers to determine alternative’s value which considered subjective. A new method named data-based fuzzy TOPSIS proposed to diminish the dependency to decision maker. The proposed algorithm use data to determine alternative’s values objectively. Subtractive Clustering (SC) and Fuzzy C-Mean (FCM) selected to transform crisp value data to fuzzy value data. Some modification applied to SC and FCM to obtain fuzzy triangular value needed by fuzzy TOPSIS. Keyword : Index Terms —Decision support systems, fuzzy TOPSIS, fuzzy C-mean, subtractive clustering }, issn = {2502-2377}, pages = {104--108} doi = {10.21456/vol3iss2pp104-108}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/6213} }
Refworks Citation Data :
Technique for Order Preference by Similarity (TOPSIS) solves multi-criteria decision making (MCDM) by ranking the alternatives. When the attributes are not deterministic, a Fuzzy TOPSIS method is applied. The traditional fuzzy TOPSIS depends on decision makers to determine alternative’s value which considered subjective. A new method named data-based fuzzy TOPSIS proposed to diminish the dependency to decision maker. The proposed algorithm use data to determine alternative’s values objectively. Subtractive Clustering (SC) and Fuzzy C-Mean (FCM) selected to transform crisp value data to fuzzy value data. Some modification applied to SC and FCM to obtain fuzzy triangular value needed by fuzzy TOPSIS.
Keyword : Index Terms—Decision support systems, fuzzy TOPSIS, fuzzy C-mean, subtractive clustering
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