Published date: May 26 2023

Journal Title: KnE Social Sciences

Issue title: International Conference on Advance & Scientific Innovation (ICASI)

Pages: 16–22

DOI: 10.18502/kss.v8i9.13315

Erlin Windia Ambarsarierlinunindra@gmail.comInformatics Department, Universitas Indraprasta PGRI, Jakarta, Indonesia

Nurfidah DwitiyantiInformatics Department, Universitas Indraprasta PGRI, Jakarta, Indonesia

Noni SelviaInformatics Department, Universitas Indraprasta PGRI, Jakarta, Indonesia

Wahyu Nur CholifahInformatics Department, Universitas Indraprasta PGRI, Jakarta, Indonesia

Putri Dina MardikaInformatics Department, Universitas Indraprasta PGRI, Jakarta, Indonesia

We compared the previous study about clustering the welfare of the Indonesian people using the Fuzzy C-Means (FCM) approach to a recent study, the Gaussian mixture model (GMM). Both of which were soft clustering. The case analyzes by classifying 34 provincial data in Indonesia, based on eight welfare of people indicator variables in 2017, which the Central Statistics Agency had issued. We compared the FCM and the GMM approaches to determine a better level of accuracy in clustering data using the Silhouette index, the Davies-Bouldin index, and the Calinski-Harabasz index values as a validity test method. The FCM and GMM methods found that the optimal clusters were 2 and 6. When we observed the consistency of the three tests’ validity results, the GMM method was preferable to the FCM clustering method.

Keywords: fuzzy, Gaussian mixture model, clustering

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