1Informatics 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, Indonesia
2School of Computing, Faculty Engineering, Universiti Teknologi Malaysia. UTM Johor Bahru, Johor 81310, Malaysia
3Information 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, Indonesia
4 Prism Lab, Insa Center Val de Loire. 88 Boulevard Lahitolle, Bourges 18000, France
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@article{JTSISKOM13998, author = {Okfalisa Okfalisa and Angraini Angraini and Shella Novi and Hidayati Rusnedy and Lestari Handayani and Mustakim Mustakim}, title = {Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {9}, number = {3}, year = {2021}, keywords = {clustering; density-based spatial clustering of applications with noise; Python; silhouette coefficient;village maturity}, abstract = {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.}, issn = {2338-0403}, pages = {133--141} doi = {10.14710/jtsiskom.2021.13998}, url = {https://jtsiskom.undip.ac.id/article/view/13998} }
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