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K-Means Clustering for Regional Segmentation of Municipal Permit Services: A Case Study of Tangerang Selatan

by Aolia Ikhwanudin, Tubagus Toifur, Muhamad Yusuf
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 125
Year of Publication: 2026
Authors: Aolia Ikhwanudin, Tubagus Toifur, Muhamad Yusuf
10.5120/ijca44904f79e07d

Aolia Ikhwanudin, Tubagus Toifur, Muhamad Yusuf . K-Means Clustering for Regional Segmentation of Municipal Permit Services: A Case Study of Tangerang Selatan. International Journal of Computer Applications. 187, 125 ( Jul 2026), 45-53. DOI=10.5120/ijca44904f79e07d

@article{ 10.5120/ijca44904f79e07d,
author = { Aolia Ikhwanudin, Tubagus Toifur, Muhamad Yusuf },
title = { K-Means Clustering for Regional Segmentation of Municipal Permit Services: A Case Study of Tangerang Selatan },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 125 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 45-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number125/k-means-clustering-for-regional-segmentation-of-municipal-permit-services-a-case-study-of-tangerang-selatan/ },
doi = { 10.5120/ijca44904f79e07d },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-29T00:34:13.747472+05:30
%A Aolia Ikhwanudin
%A Tubagus Toifur
%A Muhamad Yusuf
%T K-Means Clustering for Regional Segmentation of Municipal Permit Services: A Case Study of Tangerang Selatan
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 125
%P 45-53
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective allocation of public service resources in Indonesian cities requires understanding the spatial heterogeneity of permit-type demand at the sub-district level. This paper presents a multi-method unsupervised clustering framework applied to 56 sub-districts in Tangerang Selatan City, using proportion vectors for eight permit categories combined with log-transformed application volume. Four algorithms were evaluated — K-Means, Agglomerative (Ward), Gaussian Mixture Model (GMM), and Spectral Clustering — across k=2..6. Four outlier sub-districts were identified and excluded prior to clustering (kel_0, Alue Bagok, Pondok Benda, Pamulang Timur). On the remaining 52 sub-district, K-Means at k=4 (collapsed to 3 interpretable clusters) achieved the highest silhouette score (0.2456), outperforming Agglomerative (0.2347), GMM (0.1596), and Spectral (0.1401). The three final clusters represent: Cluster 0 (5 sub-districts) — cemetery/land-use permit specialists with high tariffed field-inspection demand (p_Field=0.448, mean 8,106 applications); Cluster 1 (28 sub-districts) — residential service generalists with high free-field inspection demand (p_FreeF=0.413, mean 3,644 applications); and Cluster 2 (19 sub-districts) — high-volume administrative-review centers with elevated inter-agency permit activity (p_Admin=0.440, mean 7,618 applications). These findings provide a data-driven basis for differentiated surveyor allocation, digital service channel design, and spatial planning prioritization at DPMPTSP Tangerang Selatan. Beyond silhouette alone, a comprehensive multi-metric evaluation (Davies–Bouldin and Calinski–Harabasz indices across k=2..6 for all four algorithms), a 500-iteration bootstrap stability analysis, and one-way ANOVA significance tests on the resulting cluster profiles were conducted to validate the robustness of the chosen segmentation.

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Index Terms

Computer Science
Information Sciences

Keywords

K-Means clustering silhouette coefficient permit segmentation sub-district Tangerang Selatan DPMPTSP outlier detection method comparison StandardScaler

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