Open AccessIdentification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise
Author(s) -
Okfalisa Okfalisa,
Angraini Angraini,
Shella Novi,
Hidayati Rusnedy,
Lestari Handayani,
Mustakim Mustakim
Publication year - 2021
Publication title -
jurnal teknologi dan sistem komputer
Language(s) - English
Resource type - Journals
eISSN - 2620-4002
pISSN - 2338-0403
DOI - 10.14710/jtsiskom.2021.13998
Subject(s) - dbscan , cluster analysis , geography , distribution (mathematics) , empowerment , data mining , computer science , mathematics , economic growth , fuzzy clustering , statistics , economics , canopy clustering algorithm , mathematical analysis
The rural development measurement is undoubtedly not easy due to its particular needs and conditions. This study classifies village performance from social, economic, and ecological indices. One thousand five hundred ninety-one villages from the Community and Village Empowerment Office at Riau Province, Indonesia, are grouped into five village maturation classes: very under-developed village, under-developed village, developing village, developed village, and independent village. To date, Density-based spatial clustering of applications with noise (DBSCAN) is utilized in mining 13 of the villages’ attributes. Python programming is applied to analyze and evaluate the DBSCAN activities. The study reveals the grouping’s silhouette coefficient values at 0.8231, thus indicating the well-being clustering performance. The epsilon and minimum points values are considered in DBSCAN evaluation with percentage splits simulation. This grouping can be used as guidelines for governments in analyzing the distribution of rural development subsidies more optimal.