DOI: https://doi.org/10.14710/jtsiskom.2021.14247

Pengelompokan wilayah menurut potensi ekonomi menggunakan modifikasi algoritme fuzzy k-prototypes untuk penentuan target pembangunan desa

Regional clustering based on economic potential with a modified fuzzy k-prototypes algorithm for village developing target determination

Badan Pusat Statistik Provinsi Jawa Tengah. Jl. Pahlawan Nomor 6, Kota Semarang, Jawa Tengah 50241, Indonesia

Received: 21 Jun 2021; Revised: 19 Aug 2021; Accepted: 20 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
The clustering algorithm can group regions based on economic potential with mixed attributes data, consisting of numeric and categorical data. This study aims to group villages according to their economic potential in determining village development targets in Demak Regency using the fuzzy k-prototypes algorithm and modified Eskin distance to measure the distance of categorical attributes. The data used are PODES2018 data and the 2019 Wilkerstat Mapping. Village clustering produces three village clusters according to their economic potential, namely low, medium, and high economic clusters. Clusters of high economic potential are located on the main transportation routes of Semarang–Kudus and Semarang–Grobogan. However, villages on the main transportation route are still included in the low economic cluster. Considering the status of the urban/rural village classification, most of these villages are included in the urban village category. The results of this clustering can be used to determine village development targets in increasing the Village Developing Index in Demak Regency.
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Keywords: clustering; mix attributes; fuzzy k-prototypes; village potential; village developing index
Funding: Badan Pusat Statistik;Pusat Pendidikan dan Pelatihan Badan Pusat Statistik

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Section: Original Research Articles
Language : ID
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