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PREDIKSI TINGKAT PENYAKIT DEMAM BERDARAH DI KOTA KENDARI MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBOR

SemanTIK: Teknik Informasi · Vol. 4 No. 1 (2018) · pp. 103–112

DOI
10.55679/semantik.v4i1.4036
ISSN
2502-8928, 2460-1446
Published
2018-07-11
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng

Abstract

Kendari city is still a dengue endemic area. It is viewed from the topography of Kendari City which has a height between 0-472 m above sea level (DPL). The low ability to anticipate the occurrence of Dengue Fever is caused, among others, because the time, place and incidence rate has not been well predicted, the unavailability of index and vulnerability maps of the area based on the time of the incident, and the unavailability of predictive prediction model of DHF incidence. This underlies the need for DHF Prediction in Kendari City.The method used to predict dengue fever rate in Kendari city that is Modified K-Nearest Neighbor method. MKNN consists of two processing, the first data validation training that aims to validate training data and the second is to apply the weighting of KNN.Based on the results of testing conducted, application prediction of dengue fever (DHF) in kendari city using Modified K-Nearest Neighbor (MKNN) method is able to predict with the smallest error value 0,04%, for value k = 4 biggest error value 1, 58 for the value of k = 4 and the smallest error rate of 0.28% for the value of k = 3.Keywords— Dengue Fever, Forecasting, Modified K-Nearst NeighborDOI:10.5281/zenodo.1402838

Collaboration network of the authors

40 authors · 111 co-authorship links · 16 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
  • Institut Teknologi Sepuluh Nopember
  • Hasanuddin University
  • Universitas Sembilanbelas November Kolaka
  • Universitas Cokroaminoto Palopo
  • Universitas Mega Buana Palopo
  • 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 Oleo6
Sutardi SutardiUniversitas Halu Oleo6
Ika Purwanti NingrumUniversitas Halu Oleo6
Jumadil NangiUniversitas Halu Oleo5
Statiswaty StatiswatyUniversitas Halu Oleo4
Suharsono BantunUniversitas Sembilanbelas November Kolaka4
Isnawaty IsnawatyUniversitas Halu Oleo4
Isnawaty IsnawatyAffiliation unknown4
Natalis RansiUniversitas Halu Oleo3
LM TajidunUniversitas Halu Oleo2
Nirsal NirsalUniversitas Cokroaminoto Palopo2
Rahmat RamadhanUniversitas Halu Oleo2
Subardin SubardinAffiliation unknown2
Muhammad Ishak JaelaniUniversitas Halu Oleo2
Nurul Hajra Hadijah HajraUniversitas Halu Oleo1
Aditya PutraUniversitas Halu Oleo1
StatiswatyUniversitas Halu Oleo1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Mohammad ZarkasiInstitut Teknologi Sepuluh Nopember1
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Nanik SuciatiInstitut Teknologi Sepuluh Nopember1
Fransisca Joanet PontohHasanuddin University1
Amil Ahmad IlhamHasanuddin University1
Ingrid NurtanioHasanuddin University1
Rabiuldien AmatUniversitas Halu Oleo1
Hastuti HastutiAffiliation unknown1
Muh. Abdi FahmiAffiliation unknown1
Niarma NiarmaUniversitas Halu Oleo1
Wa Ode ErmaliantiUniversitas Halu Oleo1
Abdul MalikUniversitas Mega Buana Palopo1
Gusti Made Cahya WiarjayaAffiliation unknown1
Azkar KumalaAffiliation unknown1
LM. Fid AksaraUniversitas Halu Oleo1
Tamsil TajsamAffiliation unknown1
Ramadhin ZaidUniversitas Halu Oleo1
Nur Imran RuslanAffiliation 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://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">PREDIKSI TINGKAT PENYAKIT DEMAM BERDARAH DI KOTA KENDARI MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBOR</dc:title>
	<dc:creator>Muri, Linda Purnama</dc:creator>
	<dc:creator>Pramono, Bambang</dc:creator>
	<dc:creator>Sari, Jayanti Yusmah</dc:creator>
	<dc:description xml:lang="en-US">Kendari city is still a dengue endemic area. It is viewed from the topography of Kendari City which has a height between 0-472 m above sea level (DPL). The low ability to anticipate the occurrence of Dengue Fever is caused, among others, because the time, place and incidence rate has not been well predicted, the unavailability of index and vulnerability maps of the area based on the time of the incident, and the unavailability of predictive prediction model of DHF incidence. This underlies the need for DHF Prediction in Kendari City.The method used to predict dengue fever rate in Kendari city that is Modified K-Nearest Neighbor method. MKNN consists of two processing, the first data validation training that aims to validate training data and the second is to apply the weighting of KNN.Based on the results of testing conducted, application prediction of dengue fever (DHF) in kendari city using Modified K-Nearest Neighbor (MKNN) method is able to predict with the smallest error value 0,04%, for value k = 4 biggest error value 1 , 58 for the value of k = 4 and the smallest error rate of 0.28% for the value of k = 3.Keywords— Dengue Fever, Forecasting, Modified K-Nearst NeighborDOI :10.5281/zenodo.1402838</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2018-07-11</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://ojs.uho.ac.id/index.php/semantik/article/view/4036</dc:identifier>
	<dc:identifier>10.55679/semantik.v4i1.4036</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol 4, No 1 (2018): semanTIK; 103-112</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:source>10.55679/semantik.v4i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/view/4036/3290</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 semanTIK</dc:rights>
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