Open AccessThe Implementation of Fuzzy-C Means to Categorize Poverty Data in Riau Province
Author(s) -
Mustakim Mustakim,
Hadi Eka Saputa,
Mutasir,
NURYANTI NURYANTI,
. Salmiah
Publication year - 2021
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 - 10.1088/1742-6596/1783/1/012013
Subject(s) - poverty , fuzzy logic , partition (number theory) , identification (biology) , government (linguistics) , computer science , cluster analysis , cluster (spacecraft) , statistics , business , econometrics , economics , mathematics , economic growth , artificial intelligence , biology , linguistics , philosophy , botany , combinatorics , programming language
Riau Province was the 13th in line as the poverty contributor in Indonesia. It was because of the fund from government was not properly distributed. Therefore, an identification system to distribute the poverty funding was needed in order to reach the citizens in poverty. Based on the available parameter, it could accelerate the eradication of poverty. Poverty caused by various factors, i.e. educational background, living environment, job and health. One of the solutions to distribute the fund properly was by categorizing the poverty data. This research used Fuzzy Clustering Means (FCM) method because every area has the possibility to become part of each cluster with different membership level between score 0 and 1. The recommended parameter was w = 2, Max_iter = 100, ε = 0.1, P0 = 10.000.0000, t = 1, C = 3 with Partition Coefficient Index (PCI) validity score 0.47. The web built based-system uses programming language PHP with Framework YII 2 Advanced. All clustered data was analyzed based on poverty variable level that was dominant in range of regency and District.