Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise

Okfalisa Okfalisa (Informatics Engineering Department, Universitas Islam Negeri Sultan Syarif Kasim Riau. Jl. HR. Soebrantas Panam Km. 15 No. 155, Tuah Madani, Kec. Tampan, Kampar Regency, Riau 28293|Universitas Islam Negeri Sultan Syarif Kasim Riau)
Angraini Angraini (School of Computing, Faculty Engineering, Universiti Teknologi Malaysia. UTM Johor Bahru, Johor 81310|Universiti Teknologi Malaysia|Malaysia Information System Department, Universitas Islam Negeri Sultan Syarif Kasim Riau. Jl. HR. Soebrantas Panam Km)
Shella Novi (Informatics Engineering Department, Universitas Islam Negeri Sultan Syarif Kasim Riau. Jl. HR. Soebrantas Panam Km. 15 No. 155, Tuah Madani, Kec. Tampan, Kampar Regency, Riau 28293|Universitas Islam Negeri Sultan Syarif Kasim Riau)
Hidayati Rusnedy (Informatics Engineering Department, Universitas Islam Negeri Sultan Syarif Kasim Riau. Jl. HR. Soebrantas Panam Km. 15 No. 155, Tuah Madani, Kec. Tampan, Kampar Regency, Riau 28293|Universitas Islam Negeri Sultan Syarif Kasim Riau)
Lestari Handayani (Informatics Engineering Department, Universitas Islam Negeri Sultan Syarif Kasim Riau. Jl. HR. Soebrantas Panam Km. 15 No. 155, Tuah Madani, Kec. Tampan, Kampar Regency, Riau 28293|Universitas Islam Negeri Sultan Syarif Kasim Riau|Indonesia Prism Lab)
Mustakim Mustakim (Information System Department, Universitas Islam Negeri Sultan Syarif Kasim Riau. Jl. HR. Soebrantas Panam Km. 15 No. 155, Tuah Madani, Kec. Tampan, Kampar Regency, Riau 28293|Universitas Islam Negeri Sultan Syarif Kasim Riau|Indonesia)

Jurnal Teknologi dan Sistem Komputer
Volume 9, Issue 3, Year 2021 (July 2021)

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.