<![CDATA[Modeling and linguistic representation in the form Interval Type-2 Fuzzy have better accuracy than Type-1 Fuzzy. The type-2 fuzzy set involves more uncertainty than the type-1 fuzzy set. The degree of fuzzy membership is used to explain uncertainty and ambiguity in the real world. This study presents the type-2 Fuzzy Analytic Network Process (ANP) method to determine the weight of each attribute based on the level of interest and the extension method of type-2 Fuzzy TOPSIS to handle problems based on the value of the fuzzy type-2 attribute. Decision-making is based on the assessment of several experts called Multi-Criteria Group Decision Making (MCGDM), using type-2 Fuzzy geometric mean aggregation function. The membership function in this research is type-2 fuzzy based on the trapezoid. The contribution is a hybrid method Type-2 Fuzzy TOPSIS with Fuzzy Type-2 ANP group-based with new metric intervals on fuzzy type-2 for decision making. The results are a hybrid type-2 FANP and FTOPSIS decision-making model to support the best decision-making. Based on a comparison of the accuracy of trapezoid model 1, model 2, and model 3, the best accuracy result is model 3, which is 84%. The research benefits by presenting a hybrid Type-2 Fuzzy TOPSIS and ANP method that improves decision-making accuracy and better handling uncertainty and ambiguity than Type-1 Fuzzy systems.]]>
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