Penerapan Algoritma Fuzzy C-Means untuk Pengelompokan Kepuasan Masyarakat terhadap Layanan Berdasarkan Dimensi SERVQUAL
Abstract
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.
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References
Afifah, Z. N., Rohimah, N. S., & Putra, R. U. (2024). Analisis Kepuasan Masyarakat Terhadap Kehadiran Mal Pelayanan Publik ( MPP ) di Kota Bandung Analysis of Public Satisfaction with the Presence of Public Service Malls ( MPP ) in Bandung City. Jurnal Administrasi Publik, 16(c), 61–75. https://doi.org/10.20473/jap.v16i1.59244
Al Hafidz, M., Ebrison, H., Baenudin, M., & Ridwan, M. (2025). Analisis Clustering Data Balita dengan Algoritma K-Means dan Fuzzy C-Means: Sebuah Studi Komparatif Menggunakan Silhouette Index. Jurnal Sistem Dan Teknologi Informasi, 13(2), 266–271. https://doi.org/10.26418/justin.v13i2.82529
Belopa, J., Ramba, D., & Pundissing, R. (2023). Analisis Tingkat Kepuasan Masyarakat Terhadap Manajemen Pelayanan Publik Pada Kantor Lembang Sa’tandung Tana Toraja Juprianto. MENAWAN : Jurnal Riset Dan Publikasi Ilmu Ekonomi, 1(6), 68–80. https://doi.org/10.61132/menawan.v1i6.69
Bezdek C., J. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York. https://link.springer.com/book/10.1007/978-1-4757-0452-5
Faturahman, R. D., & Hidayati, N. (2025). Implementasi Fuzzzy C-means Dalam Pengelompokan Tingkat Kemiskinan Pada Kabupaten/Kota Di Provinsi Jawa Tengah. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 10(1), 137–149. https://doi.org/10.29100/jipi.v10i1.5747
Fawaz, & Handhayani, T. (2025). Perbandingan Fuzzy C-means Dan K-means Pada Klasterisasi Bawang Merah. Ilmu Komputer Dan Sistem Informasi (JIKSI), 13(2), 1. https://doi.org/10.24912/jiksi.v13i2.35141
Firdaus, H. S., Nugraha, A. L., Sasmito, B., Awaluddin, M., & Nanda, C. A. (2021). Perbandingan Metode Fuzzy C-Means Dan K-Means Untuk Pemetaan Daerah Rawan Kriminalitas Di Kota Semarang. Elipsoida : Jurnal Geodesi Dan Geomatika, 04(01), 58–64. https://doi.org/10.14710/elipsoida.v4i1.9219
Indah, S., N, H., S, S., & Widiarti. (2022). Analisis Klaster Menggunakan Metode Fuzzy C-Means pada Data COVID-19 di Provinsi Lampung. Sn-Smiap-Vi, 6, 66–73. http://repository.lppm.unila.ac.id/id/eprint/42389
Mustikasari, M., Hanim, W., Mardiana, S., Haryadi, Y., & Nurrahman, A. (2023). Analisis Kepuasan Mustahik Terhadap Pelayanan Badan Zakat Nasional ( BAZNAS ) Kota Bandung. Jurnal Ilmu Multidisplin (JIM), 2(2), 179–192. https://doi.org/10.38035/jim.v2i2.314
Mutiara sari, A., & Ghufron. (2025). Penerapan Sistem Informasi Geografis Terhadap Daerah Rawan Kriminalitas Dengan Metode Fuzzy C-Means. Kohesi: Jurnal Multidisiplin Saintek, 10(4). https://doi.org/10.8734/Kohesi.v1i2.365
Ningrum, N. A., & Syaputra, M. A. (2022). Penerapan Metode Fuzzy C-means Untuk Penentuan Kompetensi Mahasiswa Di Stmik Dharmawacana Metro. Journal Computer Science and Information Systems : J-Cosys, 2(1), 19–25. https://doi.org/10.53514/jc.v2i1.289
Novetu, J. R., & Rahman, A. Z. (2023). Analisis Kepuasan Masyarakat Mal Pelayanan Publik Melalui Importance Performance Analysis DI Kabupaten Rembang. Nova Idea: Jurnal Geodesi Undip, 32(3), 167–186. https://doi.org/10.14710/nova_idea.49617
Nugraha, G. S., Dwiyansaputra, R., Bimantoro, F., & Aranta, A. (2023). Implementasi Fuzzy C-Means untuk Pengelompokan Daerah Berdasarkan Persebaran Penularan Covid-19. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIK), 10(1), 97–104. https://doi.org/10.25126/jtiik.2023105796
Nugroho, A., Utami, P. Y., & Surya, R. S. (2025). Perbandingan Algoritma K-Means Dan Fuzzy C-Means Untuk Pengelompokkan Daerah Produktivitas Tanaman Padi Kalimantan Barat Aryo. Journal Iof Innovative and Creativity, 5(2). https://doi.org/https://doi.org/10.31004/joecy.v5i2.2550
Nurdin, Putri, U. M., Aidilof, H. A.-K., & Bustami. (2022). Implementasi Fuzzy C-Means Menentukan Tingkat Kepuasan Mahasiswa Dalam Pembelajaran Online. SISTEMASI: Jurnal Sistem Informasi, 11(1), 121. https://doi.org/10.32520/stmsi.v11i1.1638
Ramadhan, A., Purwanti, T., & Dwisnu, E. (2026). Tingkat Kepuasan Masyarakat Terhadap Kualitas Pelayanan Administrasi Kependudukan Di Kelurahan Penggantungan. Jurnal STIA Bengkulu, 12(25), 141–152. https://doi.org/https://doi.org/10.56135/jsb.v12i1.301
Rosmiati, N., Septiana, T., & Rachmadio, R. E. (2024). Analisis Kepuasan Masyarakat Terhadap Pelayanan Publik Berdasarkan Indeks Kepuasan Masyarakat di Kantor Kecamatan Leuwiliang Kabupaten Bogor. Journal of Human And Education (JAHE), 1(2), 13–22. https://doi.org/10.48093/jiask.v1i2.8
Supawanhar, Putri, S., & Febriansah, R. R. (2024). Pengukuran Indeks Kepuasan Masyarakat Perizinan E-Mall Pelayanan Publik (MPP) Kota Bengkulu. Journal of Governance and Public Administration (JoGaPA), 1(4), 563–578. https://doi.org/https://doi.org/10.70248/jogapa.v1i4.1237
Telaumbanua, S. A. B., Setiadi, F., & Nurjanah, S. (2025). Analisis Clustering Menggunakan Metode Enhanced Fuzzy C-Means Clustering Dengan Algoritma Rock Pada Student Performance Dataset. Bit-Tech (Binary Digital - Technology), 7(3), 984–994. https://doi.org/10.32877/bt.v7i3.2287
Wisanta, E. H., & Marlim, Y. N. (2021). Analisis Algoritma K-Means Untuk Clustering Kepuasan Pelayanan : Mall Pelayanan Publik Pekanbaru. Seminar Nasional Informatika (SENATIKA). https://www.ejournal.pelitaindonesia.ac.id/index.php/SENATIKA/article/view/1160
Zein, J. R., Ramdhan, W., & Sahren. (2024). Penerapan Metode Servqual Dalam Meningkatkan Kualitas Kinerja Pelayanan Publik Pada Kantor Urusan Agama Kecamatan Kawang Panca Arga. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 9(4), 2459–2472. https://doi.org/10.29100/jipi.v9i4.6254
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