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Implementasi DBSCAN dan Topsis Pada Sistem Pengelompokkan Kriminalitas Berbasis Wilayah Berdasarkan Kepadatan Penduduk di Kota Kendari

SemanTIK: Teknik Informasi · Vol. 12 No. 1 (2026) · pp. 110–118

DOI
10.55679/semantik.v12i1.296
ISSN
2502-8928, 2460-1446
Published
2026-06-25
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Kriminalitas di kawasan perkotaan, seperti Kota Kendari, menunjukkan pola yang rumit dan terus berubah, baik dari segi jenis kejahatan, waktu kejadian, maupun lokasi. Situasi ini mendorong kebutuhan akan sistem analisis spasial yang andal untuk membantu otoritas dalam merancang langkah pencegahan yang tepat. Penelitian ini menghadirkan solusi melalui pengembangan sistem informasi geografis yang menggabungkan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN) guna mengelompokkan wilayah rawan kejahatan dan metode Density-Based Spatial Clustering of Applications with Noise (TOPSIS) untuk menentukan urutan prioritas penanganan. Hasilnya menunjukkan bahwa DBSCAN mampu mengenali klaster dengan bentuk dan kepadatan beragam termasuk kelompok kejahatan yang tersebar di antaranya pencurian dengan kekerasan, pencurian sepeda motor, penganiayaan, pelanggaran terkait narkoba, serta pemerkosaan namun saling terhubung serta klaster kecil yang terpisah di tengah area padat dengan kualitas pengelompokan yang sangat baik, sebagaimana tercermin dari nilai Davies-Bouldin Index (DBI) sebesar 0,4482, yang menandakan klaster yang padat secara internal dan terpisah secara jelas. Sementara itu, pendekatan TOPSIS berhasil memberikan peringkat objektif terhadap kecamatan berdasarkan empat indikator: frekuensi kejadian, tipe kejahatan, besaran kerugian, dan kepadatan penduduk. Kota kendari pada wilayah Kecamatan Kadia dan Wua Wua muncul sebagai wilayah dengan tingkat prioritas tertinggi. Temuan ini menegaskan manfaat sistem dalam mendukung strategi pencegahan kriminalitas yang berbasis data serta pengoptimalan distribusi sumber daya di wilayah perkotaan. Crime in urban areas, such as the city of Kendari, exhibits complex and constantly changing patterns in terms of crime types, times of occurrence, and locations. This situation underscores the need for a reliable spatial analysis system to assist authorities in designing appropriate preventive measures. This study presents a solution through the development of a geographic information system that combines the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster crime-prone areas and the Density-Based Spatial Clustering of Applications with Noise (TOPSIS) method to determine the priority order for intervention. The results show that DBSCAN is capable of identifying clusters with diverse shapes and densities, including interconnected groups of crimes such as robbery, motorcycle theft, assault, drug-related offenses, and rape, as well as small, isolated clusters within densely populated areas, with excellent clustering quality, as reflected by a Davies -Bouldin Index (DBI) value of 0.4482, indicating clusters that are internally dense and clearly separated. Meanwhile, the TOPSIS approach successfully provided an objective ranking of subdistricts based on four indicators: incident frequency, crime type, extent of loss, and population density. The city of Kendari, specifically the Kadia and Wua Wua subdistricts, emerged as the area with the highest priority level

Keywords

Collaboration network of the authors

40 authors · 121 co-authorship links · 2 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sutardi SutardiUniversitas Halu Oleo7
Jumadil NangiUniversitas Halu Oleo5
Adha Mashur SajiahUniversitas Halu Oleo5
Statiswaty StatiswatyUniversitas Halu Oleo4
Isnawaty IsnawatyUniversitas Halu Oleo4
Natalis RansiUniversitas Halu Oleo3
Ika Purwanti NingrumUniversitas Halu Oleo3
Isnawaty IsnawatyAffiliation unknown3
Rizal Adi SaputraUniversitas Halu Oleo3
LM TajidunUniversitas Halu Oleo2
Rahmat RamadhanUniversitas Halu Oleo2
Subardin SubardinAffiliation unknown2
Muhammad Ishak JaelaniUniversitas Halu Oleo2
Doddy ZulverdiAffiliation unknown2
Erwin Gunawan HutapeaAffiliation unknown2
Bambang KusmiarsoAffiliation unknown2
Saraswati SaraswatiUniversitas Halu Oleo2
Handri AdiwilagaAffiliation unknown2
Syachman PerdymerAffiliation unknown2
Nurkholisoh Ibnu AmanAffiliation unknown2
Illinia Ayudhia RiyadiAffiliation unknown2
Rio KhasanandaAffiliation unknown2
Nurul Hajra Hadijah HajraUniversitas Halu Oleo1
Hastuti HastutiHasanuddin University1
Jayanti Yusmah SariUniversitas Halu Oleo1
Linda Purnama MuriUniversitas Halu Oleo1
Niarma NiarmaUniversitas Halu Oleo1
Wa Ode ErmaliantiUniversitas Halu Oleo1
Gusti Made Cahya WiarjayaAffiliation unknown1
Azkar KumalaAffiliation unknown1
LM. Fid AksaraUniversitas Halu Oleo1
Tamsil TajsamAffiliation unknown1
Ramadhin ZaidUniversitas Halu Oleo1
Nur Imran RuslanAffiliation unknown1
Masna JumiyatiAffiliation unknown1
La Ode HasnuddinAffiliation unknown1
Holis HermansyahUniversitas Halu Oleo1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://semantik.uho.ac.id/index.php/journal/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Implementasi DBSCAN dan Topsis Pada Sistem Pengelompokkan Kriminalitas Berbasis Wilayah Berdasarkan Kepadatan Penduduk di Kota Kendari</dc:title>
	<dc:creator>Aditya Putra</dc:creator>
	<dc:creator>Statiswaty</dc:creator>
	<dc:creator>Bambang Pramono</dc:creator>
	<dc:subject xml:lang="en-US">Kriminalitas</dc:subject>
	<dc:subject xml:lang="en-US">Algoritma DBSCAN</dc:subject>
	<dc:subject xml:lang="en-US">Metode TOPSIS</dc:subject>
	<dc:subject xml:lang="en-US">Wilayah Prioritas</dc:subject>
	<dc:description xml:lang="en-US">Kriminalitas di kawasan perkotaan, seperti Kota Kendari, menunjukkan pola yang rumit dan terus berubah, baik dari segi jenis kejahatan, waktu kejadian, maupun lokasi. Situasi ini mendorong kebutuhan akan sistem analisis spasial yang andal untuk membantu otoritas dalam merancang langkah pencegahan yang tepat. Penelitian ini menghadirkan solusi melalui pengembangan sistem informasi geografis yang menggabungkan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN) guna mengelompokkan wilayah rawan kejahatan dan metode Density-Based Spatial Clustering of Applications with Noise (TOPSIS) untuk menentukan urutan prioritas penanganan. Hasilnya menunjukkan bahwa DBSCAN mampu mengenali klaster dengan bentuk dan kepadatan beragam termasuk kelompok kejahatan yang tersebar di antaranya pencurian dengan kekerasan, pencurian sepeda motor, penganiayaan, pelanggaran terkait narkoba, serta pemerkosaan namun saling terhubung serta klaster kecil yang terpisah di tengah area padat dengan kualitas pengelompokan yang sangat baik, sebagaimana tercermin dari nilai Davies-Bouldin Index (DBI) sebesar 0,4482, yang menandakan klaster yang padat secara internal dan terpisah secara jelas. Sementara itu, pendekatan TOPSIS berhasil memberikan peringkat objektif terhadap kecamatan berdasarkan empat indikator: frekuensi kejadian, tipe kejahatan, besaran kerugian, dan kepadatan penduduk. Kota kendari pada wilayah Kecamatan Kadia dan Wua Wua muncul sebagai wilayah dengan tingkat prioritas tertinggi. Temuan ini menegaskan manfaat sistem dalam mendukung strategi pencegahan kriminalitas yang berbasis data serta pengoptimalan distribusi sumber daya di wilayah perkotaan.
Crime in urban areas, such as the city of Kendari, exhibits complex and constantly changing patterns in terms of crime types, times of occurrence, and locations. This situation underscores the need for a reliable spatial analysis system to assist authorities in designing appropriate preventive measures. This study presents a solution through the development of a geographic information system that combines the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster crime-prone areas and the Density-Based Spatial Clustering of Applications with Noise (TOPSIS) method to determine the priority order for intervention. The results show that DBSCAN is capable of identifying clusters with diverse shapes and densities, including interconnected groups of crimes such as robbery, motorcycle theft, assault, drug-related offenses, and rape, as well as small, isolated clusters within densely populated areas, with excellent clustering quality, as reflected by a Davies -Bouldin Index (DBI) value of 0.4482, indicating clusters that are internally dense and clearly separated. Meanwhile, the TOPSIS approach successfully provided an objective ranking of subdistricts based on four indicators: incident frequency, crime type, extent of loss, and population density. The city of Kendari, specifically the Kadia and Wua Wua subdistricts, emerged as the area with the highest priority level</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2026-06-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://semantik.uho.ac.id/index.php/journal/article/view/296</dc:identifier>
	<dc:identifier>10.55679/semantik.v12i1.296</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 12 No. 1 (2026): Vol. 12 No. 1 (2026): SemanTIK Teknik Informasi; 110-118</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://semantik.uho.ac.id/index.php/journal/article/view/296/68</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Aditya Putra, Statiswaty, Bambang Pramono</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>