Melola Discovery

← Melola Discovery

Jurnal SINTA 2

Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 1 (2017) · pp. 21–37

DOI
10.5614/itbj.ict.res.appl.2017.11.1.2
ISSN
2338-5499, 2337-5787
Published
2017-04-30
Publisher
DRI - ITB
Language
eng

Abstract

Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Context-based clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.

Collaboration network of the authors

19 authors · 30 co-authorship links · 6 across different organisations

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

  • Bandung Institute of Technology
  • Badan Pusat Statistik
  • Telkom University
  • Cohere (Canada)
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dwi Hendratmo WidyantoroBandung Institute of Technology2
Aswin JuariBandung Institute of Technology1
Novi YuslianiBandung Institute of Technology1
Hari Bagus FirdaudAffiliation unknown1
Zakiy Firdaus AlfikriBandung Institute of Technology1
Untari Novia WisestyTelkom University1
Tati Rajab MengkoBandung Institute of Technology1
Adi PancoroBandung Institute of Technology1
Afrida HelenBandung Institute of Technology1
Fariska Zakhralativa RuskandaBandung Institute of Technology1
Arief Purnama MuharramBandung Institute of Technology1
Ubaidillah Ariq PrathamaBandung Institute of Technology1
Samuel CahyawijayaCohere (Canada)1
Nugraha Priya UtamaBandung Institute of Technology1
Muh. Hafizh IzzaturrahimAffiliation unknown1
Ida Bagus Peling PrayogaAffiliation unknown1
Andika Naufal HilmyAffiliation unknown1

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://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Improvement of Fuzzy Geographically Weighted Clustering-Ant Colony Optimization Performance using Context-Based Clustering and CUDA Parallel Programming</dc:title>
	<dc:creator>Nurmala, Nila</dc:creator>
	<dc:creator>Purwarianti, Ayu</dc:creator>
	<dc:description xml:lang="en-US">Geo-demographic analysis (GDA) is the study of population characteristics by geographical area. Fuzzy Geographically Weighted Clustering (FGWC) is an effective algorithm used in GDA. Improvement of FGWC has been done by integrating a metaheuristic algorithm, Ant Colony Optimization (ACO), as a global optimization tool to increase the clustering accuracy in the initial stage of the FGWC algorithm. However, using ACO in FGWC increases the time to run the algorithm compared to the standard FGWC algorithm. In this paper, context-based clustering and CUDA parallel programming are proposed to improve the performance of the improved algorithm (FGWC-ACO). Context-based clustering is a method that focuses on the grouping of data based on certain conditions, while CUDA parallel programming is a method that uses the graphical processing unit (GPU) as a parallel processing tool. The Indonesian Population Census 2010 was used as the experimental dataset. It was shown that the proposed methods were able to improve the performance of FGWC-ACO without reducing the clustering quality of the original method. The clustering quality was evaluated using the clustering validity index.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-04-30</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://journals.itb.ac.id/index.php/jictra/article/view/2936</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.1.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 1 (2017); 21-37</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/2936/2508</dc:relation>
</oai_dc:dc>