<![CDATA[Pekanbaru Public Service Mall (MPP) is an integrated service facility that brings together various government agencies in one location. The problem identified is the absence of an in-depth mapping of community satisfaction levels that can realistically represent satisfaction gradations, as the previous approach using K-Means Clustering is crisp in nature and unable to represent the subjective satisfaction of humans who may belong to more than one category simultaneously. Therefore, this study aims to cluster community satisfaction levels toward MPP Pekanbaru services based on five SERVQUAL dimensions using Fuzzy C-Means, and to identify service dimensions that require priority improvement. Unlike K-Means, Fuzzy C-Means allows each respondent to hold membership degrees in multiple clusters simultaneously, making it more suitable for multidimensional satisfaction data. Data were collected through questionnaires distributed to 532 respondents with 23 Likert-scale items (1–5) in accordance with five SERVQUAL dimensions and PermenPANRB Number 14 of 2017. The optimal number of clusters was determined using the Partition Coefficient Index (PCI) by testing four scenarios (c=2, 3, 4, 5). PCI evaluation results showed that c=2 is the optimal configuration with the highest PCI value of 0.799303, achieving convergence at the 12th iteration. Clustering results revealed that 283 respondents (53.2%) belong to Cluster 1 labeled Very Satisfied and 249 respondents (46.8%) belong to Cluster 2 labeled Satisfied. Per-dimension SERVQUAL analysis identified Responsiveness as the primary improvement priority with the largest inter-cluster gap (1.1857 points). The contribution of this research is to produce a Fuzzy C-Means-based community satisfaction clustering model capable of representing satisfaction gradations more realistically than crisp approaches, and to provide a SERVQUAL-based service improvement priority map that can serve as an evaluation reference for MPP Pekanbaru management and other public service institutions.]]>
