## 1 Introduction
The main purpose of MCDM is to select the most important alternative, which is signified as the best overall value by joining the evaluation criteria values and weights to evaluation characteristics [16; 17]. The purpose of MCDM in this research is not to select the alternative but to acquire the weights of criteria (indicators). The command methods of MCDM include: technique for order preference by similarity to ideal solution (TOPSIS), simple additive weighting (SAW), analytic hierarchy process (AHP), and the Analytic Network Process (ANP). Among these methods, the research steps of AHP and ANP can be divided into four steps: constructing the hierarchical structure, comparing the criteria, analyzing the weight of criteria, and then ranking the alternatives. [PERSON] (2019) analyzed the weights of criteria of a decision problem that consists of selecting the location and design of a wind farm by means of the ANP method with the considered cluster of alternatives and without it and demonstrates that taking into consideration the cluster of alternatives in the decision model influences the result of criteria weights [18]. In the cases of research problems without ranking the alternatives, AHP and ANP are widely used to analyze the weights of indicators [19; 20; 21; 22; 23]. The AHP is proposed by [PERSON] [24] and the ANP is extended from AHP [25]. The AHP method only takes into account hierarchical dependencies between criteria and sub-criteria but ANP also considers the external dependency, internal correlation between criteria, and the feedback relationship of the cluster. Therefore, the ANP can construct a much more complex model than AHP but the complex model produces a much larger number of questions of the questionnaire [26]. Unlike the predefined weights of criteria shared evenly from the cluster in AHP, ANP predefines the weights, according to the situation of dependency and the final weights of criteria (sub-criteria) that change significantly with reference to predefined values. The effect of weight changes takes place in the case of sub-criteria, which are not mutually dependent on each other. In general, the weight of a sub-criterion, which other sub-criteria influence is increasing. Consequently, the weight of another sub-criteria in a given cluster is decreasing [18]. Although AHP and ANP have been developed and widely used, they still suffer from some theoretical disputes and have weaknesses. The weaknesses including: the rank reversal problem, the priorities derivation method, and the comparison scale [27]. The rank reversal problem and priorities derivation method refer to the preferences aggregation method used in the AHP/ANP. The rankings of alternatives and criteria obtained by using the right and left eigenvector methods for preferences aggregation from pairwise comparison matrices may be rendered differently. As for the comparison scale, several scales have been proposed. The choice of the \"best\" scale is a very heated debate. Some scientists argue that the choice depends on the person and the decision problem [26]. According to the advantages of ANP and the high reliability and validity of the SERVQUAL scale, even in recent years, there are many scholars doing research by ANP combined with SERVQUAL. For example, [PERSON] [28] analyzed the guidelines of airline service quality by the Decision Making Trial and Evaluation Laboratory (DEMATEL) and ANP. [PERSON] [29] researched the mobile phone service quality guidelines and customer satisfaction by ANP. Some scholars further integrated ANP with other research methods to propose new models. [PERSON] et al. [30] proposed a multi-criteria temporary facility layout-planning model, which effectively transforms the spatial layout-planning problem into a mathematical decision problem by integrating ANP and simulation-based sensitivity evaluation. Sometimes, in order to gather the information from the questionnaire precisely, the Fuzzy Analytic Network Process (FANP) is applied by integrating the fuzzy theory and ANP.