Grouping Districts/Cities in Kalimantan Island Based on The People's Welfare Indicators Using Fuzzy C-Means and Subtractive Fuzzy C-Means Methods

Abstract

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.

Clustering Wilayah Kerawanan Stunting Menggunakan Metode Fuzzy Subtractive Clustering

“…Metode ini banyak dipakai untuk menemukan pola dataset, baik menggunakan label ataupun tanpa label (15). Klastering bersifat tidak terawasi (unspervised) sehingga dapat digunakan pada dataset yang tidak memiliki variabel target ataupun dataset yang tidak diketahui kaitan antarvariabelnya (16,17). Salah satu metode clustering yang tidak terawasi ini adalah Fuzzy clustering.…”