Penerapan Metode Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Kesejahteraan Rakyat Tahun 2020

Deviyana Nurmin; Memi Nor Hayati; Rito Goejantoro

doi:10.30872/eksponensial.v13i2.1068

Deviyana Nurmin Laboratorium Statistika Terapan, FMIPA, Universitas Mulawarman
Memi Nor Hayati Laboratorium Statistika Terapan, FMIPA, Universitas Mulawarman
Rito Goejantoro Laboratorium Statistika Komputasi, FMIPA, Universitas Mulawarman

DOI: https://doi.org/10.30872/eksponensial.v13i2.1068

Abstract

Clustering is a method of grouping data into several clusters or groups so that data in one cluster has a high level of similarity and data between clusters has a low level of similarity. The clustering method used in this research is Fuzzy C-Means (FCM). FCM is a data grouping technique in which the existence of each data point in a cluster is determined by the degree of membership. To optimize the grouping results, it is necessary to validate the number of clusters using Partition Coefficient (PC). The purpose of this study is to obtain optimal grouping results from the FCM method using the PC validity indices from the people's welfare indicator data in 56 regencies/cities on the island of Kalimantan in 2020. Based on the results of the analysis, the conclusion is that the optimal number of clusters is three clusters. The first cluster consists of 24 regencies/cities on the island of Kalimantan, the second cluster consists of 17 regencies/cities on the island of Kalimantan, and the third cluster consists of 15 regencies/cities on the island of Kalimantan.

Downloads

Download data is not yet available.

References

Alwi, W., & Hasrul, M. (2018). Analisis Klaster untuk Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kesejahteraan Rakyat. Jurnal MSA, 6(1), 35-42.
Basri, F., & Munandar, H. (2009). Lanskap Ekonomi Indonesia. Jakarta: Kencana.
Bezdek, J., & Dunn, J. (1975). Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions. IEEE Transactions on Computers, 835-838.
Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. New Jersey: Prentice-Hall Inc.
Kusumadewi, S., & Purnomo, H. (2010). Aplikasi Logika Fuzzy untuk Pendukung Keputusan Edisi 2. Yogyakarta: Graha Ilmu.
Prasetyo, E. (2012). Data Mining: Konsep dan Aplikasi menggunakan Matlab. Yogyakarta: Andi Offset.
Prasetyo, S., Mustafid, & Hakim, A. (2020). Penerapan Fuzzy C-Means Kluster untuk Segmentasi Pelanggan E-Commerce dengan Metode Recency Frequency Monetary (RFM). Jurnal Gaussian, 421-433.
Setiawan, R., Ernawati, & Efendi, R. (2018). Klasifikasi Kawasan Permukiman Tingkat Kelurahan Untuk Pembangunan Sistem Berbasis Data Kualitas Permukiman (Studi Kasus: 67 Kelurahan di Kota Bengkulu). Jurnal Pseudocode, 5(1), 45-55.
Tan, P., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Boston: Pearson Education.
Wang, W., & Zhang, Y. (2007). On Fuzzy Cluster Validity Indices. Fuzzy Sets System, 158, 2095-2117.
Widarjono, A. (2015). Analisis Multivariat Terapan dengan Program SPSS, AMOS, dan SMARTPLS Edisi Kedua. Yogyakarta: UPM STIM YKPN.
Zhao, Q., & Franti, P. (2014). WB-index : A Sum-of-Squares Based Index for Cluster Validity. Data & Knowledge Engineering Elsevier B.V, 77-89.