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.
Highlights
Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects
The grouping method used in this research is the
The two grouping methods were applied to the people's welfare indicator data in 42 regencies
Summary
Data mining adalah istilah yang digunakan untuk menggambarkan penemuan pengetahuan dalam database. Fuzzy C-Means merupakan metode pengelompokan yang banyak digunakan berdasarkan fungsi tujuan, yang telah digunakan dalam berbagai aplikasi [16]. FCM adalah sebuah metode dimana diasumsikan setiap titik data berasosiasi dengan semua klaster dengan nilai fungsi keanggotaan fuzzy. Metode FCM mempunyai kelemahan, diantaranya membutuhkan kelompok yang banyak dan nilai keanggotaan ditentukan sebelumnya [8]. Dalam penelitian [9] memperoleh kesimpulan bahwa secara umum metode SFCM dapat memberikan solusi lebih baik daripada metode FCM dan tingkat kecepatan yang diberikan lebih tinggi mengenai konvergensi fungsi objektif. Berdasarkan latar belakang tersebut, maka peneliti mengelompokkan Kabupaten/Kota di Pulau Kalimantan berdasarkan indikator kesejahteraan rakyat menggunakan metode fuzzy cmeans dan subtractive fuzzy c-means sehingga hasil pengelompokan tersebut dapat membantu.
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