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Open access Jurnal SINTA 3

APLIKASI DAERAH RAWAN PENYAKIT MENGGUNAKAN ALGORITMA COMPLETE LINKAGE CLUSTERING

SemanTIK: Teknik Informasi · Vol. 5 No. 1 (2019) · pp. 105–112

DOI
10.55679/semantik.v5i1.6033
ISSN
2502-8928, 2460-1446
Published
2019-06-30
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Kendari City Health Office is a group that has one task to control the spread of a certain particular disease. To carry out its duties and functions, ideally, the Health Office must have the ability to divide areas that are vulnerable and not prone to disease. To facilitate the dividing, this study was made which aims to build an application that is implemented using the complete-linkage clustering method by forming clusters of data on the number of cases of the disease in 10 sub-districts in Kendari City in 2012 to 2016. Identified diseases are DHF, Diarrhea, and TB. Algorithm Complete Linkage Clustering is a group analysis method that attempts to build a hierarchy of data groups, grouping data with two or more objects that have the closest similarity, then proceed to another object that has a second closeness. The results showed that the areas prone to DHF were Kadia and Wua-Wua sub-districts with an index value of 47.80. Areas that are prone to Diarrhea disease are Puuwatu sub-district with an index value of 1181.40, area which is susceptible to TB disease is West Kendari sub-district with an index value of 92.40. The system can group and map disease-prone areas in the city of Kendari, using Complete Linkage Clustering.Keywords—Complete-Linkage Clustering, Health Service, Dengue Fever, Diarrhea, Tuberculosis (TBC) DOI: 10.5281/zenodo.3183951

Collaboration network of the authors

40 authors · 129 co-authorship links · 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
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Adha Mashur SajiahUniversitas Halu Oleo12
La SurimiUniversitas Halu Oleo11
Arman ArmanUniversitas Halu Oleo7
Bambang PramonoUniversitas Halu Oleo7
Edi CahyonoUniversitas Halu Oleo6
LM. Fid AksaraUniversitas Halu Oleo5
Sutardi SutardiUniversitas Halu Oleo5
ARMAN FASLIHAffiliation unknown5
Rahmat RamadhanUniversitas Halu Oleo4
L.M. Fid AksaraUniversitas Halu Oleo4
Andi TenriawaruUniversitas Halu Oleo3
Isnawaty IsnawatyAffiliation unknown3
Umar RamdaniUniversitas Halu Oleo3
Halim HalimUniversitas Halu Oleo3
Gusti Arviana RahmanUniversitas Halu Oleo2
Muhammad ArfanAffiliation unknown2
Fitria Rihin UyunUniversitas Halu Oleo2
Statiswaty StatiswatyUniversitas Halu Oleo2
Yuwanda PurnamasariAffiliation unknown2
Muhammad Zakaria UmarAffiliation unknown2
Ambo Wonua NusantaraAffiliation unknown2
Rosliana EsoAffiliation unknown2
Dian Safitri DurukaUniversitas Halu Oleo1
Wa Ode IrmayadaniUniversitas Halu Oleo1
Umu KhabibahUniversitas Halu Oleo1
Amal AnugraUniversitas Halu Oleo1
Gunawan GunawanUniversitas Halu Oleo1
La Ode Muhammad Hafidz Abdillah Sam MongkitoUniversitas Halu Oleo1
Budi Wijaya RaufUniversitas Halu Oleo1
Ferdinand Murni HamunduUniversitas Halu Oleo1
Fazlul Rachmat MubbaraqUniversitas Halu Oleo1
Sri Ayu LestariAffiliation unknown1
Achirul Slamet SugiartoAffiliation unknown1
La Ode SaidiAffiliation unknown1
Ferdinand Murni HamunduAffiliation unknown1
SahrulAffiliation unknown1
Nurul Hajra Hadijah HajraUniversitas 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://ojs.uho.ac.id/index.php/semantik/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">APLIKASI DAERAH RAWAN PENYAKIT MENGGUNAKAN ALGORITMA COMPLETE LINKAGE CLUSTERING</dc:title>
	<dc:creator>Ramadhani, Uun Amalia</dc:creator>
	<dc:creator>Ransi, Natalis</dc:creator>
	<dc:creator>Nangi, Jumadil</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">Kendari City Health Office is a group that has one task to control the spread of a certain particular disease. To carry out its duties and functions, ideally, the Health Office must have the ability to divide areas that are vulnerable and not prone to disease. To facilitate the dividing, this study was made which aims to build an application that is implemented using the complete-linkage clustering method by forming clusters of data on the number of cases of the disease in 10 sub-districts in Kendari City in 2012 to 2016. Identified diseases are DHF, Diarrhea, and TB. Algorithm Complete Linkage Clustering is a group analysis method that attempts to build a hierarchy of data groups, grouping data with two or more objects that have the closest similarity, then proceed to another object that has a second closeness. The results showed that the areas prone to DHF were Kadia and Wua-Wua sub-districts with an index value of 47.80. Areas that are prone to Diarrhea disease are Puuwatu sub-district with an index value of 1181.40, area which is susceptible to TB disease is West Kendari sub-district with an index value of 92.40. The system can group and map disease-prone areas in the city of Kendari, using Complete Linkage Clustering.Keywords—Complete-Linkage Clustering, Health Service, Dengue Fever, Diarrhea, Tuberculosis (TBC) DOI : 10.5281/zenodo.3183951</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
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	<dc:date>2019-06-30</dc:date>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/semantik/article/view/6033</dc:identifier>
	<dc:identifier>10.55679/semantik.v5i1.6033</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol 5, No 1 (2019): semanTIK; 105-112</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:source>10.55679/semantik.v5i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/view/6033/pdf</dc:relation>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/downloadSuppFile/6033/406</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 semanTIK</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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