Open AccessIndonesian community welfare levels clustering using the fuzzy subtractive clustering (FCM) method
Author(s) -
P. Laras Sakti Cahyaningrum,
Nursyiva Irsalinda
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1373/1/012036
Subject(s) - cluster analysis , subtractive color , fuzzy clustering , welfare , entropy (arrow of time) , indonesian , complete linkage clustering , data mining , centroid , fuzzy logic , cluster (spacecraft) , mathematics , computer science , artificial intelligence , cure data clustering algorithm , political science , law , physics , linguistics , philosophy , quantum mechanics , optics , programming language
Clustering is a technique used to classify objects or cases into groups based on their similarity, called clusters or groups. Objects in each group tend to resemble each other and differ greatly (not the same) with objects from other clusters. Public welfare is a condition of fulfilling the material, spiritual and social needs of citizens in order to be able to live properly. Fuzzy Subtractive Clustering (FSC) method is a clustering algorithm that can form the number and centroid of clusters in accordance with data conditions. This study aims to determine the FSC results in grouping the level of welfare of the Indonesian people in 2017. The testing results of the cluster validity index show 2 values of Partition Entropy and Classification Entropy forming into 2 clusters that have the best value, indicating that the provincial group has a high welfare level and the provincial group has a low welfare level.