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Fuzzy Time Series Saxena Easo on Forecasting the Number of Dengue Hemorrhagic Fever Cases in Bengkulu Province

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 1 (2025) · pp. 243–255

DOI
10.20956/j.v22i1.45365
ISSN
2614-8811, 1858-1382
Published
2025-09-08
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Dengue Hemorrhagic Fever (DHF) is a viral infectious disease that is transmitted through mosquitoes and is a public health problem in the world. Untreated Dengue Hemorrhagic Fever (DHF) can trigger extraordinary events (KLB), severe dengue and even cause death. Tropical countries like Indonesia cause cases of Dengue Hemorrhagic Fever (DHF) to be found in almost all provinces. This disease is endemic, especially in Bengkulu Province. The increase and decrease in the number of cases can also be seen using forecasting methods. The time series forecasting method used in this research is the Saxena-Easo fuzzy time series. Where this method is a modification and refinement of the previous method, namely the Stevenson & Porter fuzzy time series method. The Stevenson & Porter method changes the actual data into percentage change which was then developed by Saxena Easo with modifications to the number of data intervals. The data developed by Saxena Easo is dividing the number of intervals into sub-intervals based on the number of frequencies. This research aims to predict the number of Dengue Hemorrhagic Fever cases in Bengkulu Province from the 1st week of 2022 to the 24th week of 2024, and compare it with existing data on the number of cases. Based on the research, the results of the accuracy level of the Mean Absolute Percentage Error (MAPE) in forecasting the number of cases of Dengue Hemorrhagic Fever in Bengkulu Province using the FTS Saxena Easo method obtained a value of 0.359637893 or 3.5%, so the forecast accuracy value obtained is included in the criteria for very good forecasting, because it has an accuracy value below 10%. It can be said that the comparison between actual data and forecasts is not much different.

Keywords

Collaboration network of the authors

40 authors · 121 co-authorship links · showing the 40 most connected

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  • Hasanuddin University
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Co-authorOrganisationJoint articles
Nur AfandiHasanuddin University6
Jose RizalAffiliation unknown3
Zulfia Memi MayasariAffiliation unknown2
Anggel Dwi MirandaAffiliation unknown2
Vivin AcnesyaAffiliation unknown2
Khairul Anam BahrurAffiliation unknown1
Malikah UmarAffiliation unknown1
Rudi TriyonoAffiliation unknown1
Rindi Anti Nur FadhilahAffiliation unknown1
SRI WAHYUNIAffiliation unknown1
Lidya SavitriAffiliation unknown1
Meca NerdikaAffiliation unknown1
Fajri RamadanAffiliation unknown1
Nova Asri RamdhaniAffiliation unknown1
Arini RatnasariAffiliation unknown1
Mila KarmilaAffiliation unknown1
Indria AugustinaAffiliation unknown1
Grandiano Escool TariganAffiliation unknown1
Ryan Ferdinand SitohangAffiliation unknown1
Arif JabalAffiliation unknown1
Herlin FransiskaAffiliation unknown1
Winalia AgwilAffiliation unknown1
Pepi NoviantiAffiliation unknown1
Etis SunandiAffiliation unknown1
Anggun PermatasariAffiliation unknown1
Alya SaputriAffiliation unknown1
Lutfiah FirlianAffiliation unknown1
Indah Srilita AsmuyanaAffiliation unknown1
Fachri FaisalAffiliation unknown1
Mulia AstutiAffiliation unknown1
Refpo RahmanAffiliation unknown1
Mochammad YusaAffiliation unknown1
Asep Sofwan F. AlqapAffiliation unknown1
HelmizarAffiliation unknown1
Ramya RachmawatiAffiliation 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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Fuzzy Time Series Saxena Easo on Forecasting the Number of Dengue Hemorrhagic Fever Cases in Bengkulu Province</dc:title>
	<dc:creator xml:lang="en">Damayanti, Septri</dc:creator>
	<dc:creator xml:lang="en">Yosmar, Siska</dc:creator>
	<dc:creator xml:lang="en">Hidayati, Nurul</dc:creator>
	<dc:creator xml:lang="en">Widayati, Ratna</dc:creator>
	<dc:creator xml:lang="en">Warrahma, Bunga Aulya</dc:creator>
	<dc:subject xml:lang="en">Dengue Hemorrhagic Fever (DHF)</dc:subject>
	<dc:subject xml:lang="en">Kota Bengkulu</dc:subject>
	<dc:subject xml:lang="en">Forecasting</dc:subject>
	<dc:subject xml:lang="en">Fuzzy Time Series</dc:subject>
	<dc:description xml:lang="en">Dengue Hemorrhagic Fever (DHF) is a viral infectious disease that is transmitted through mosquitoes and is a public health problem in the world. Untreated Dengue Hemorrhagic Fever (DHF) can trigger extraordinary events (KLB), severe dengue and even cause death. Tropical countries like Indonesia cause cases of Dengue Hemorrhagic Fever (DHF) to be found in almost all provinces. This disease is endemic, especially in Bengkulu Province. The increase and decrease in the number of cases can also be seen using forecasting methods. The time series forecasting method used in this research is the Saxena-Easo fuzzy time series. Where this method is a modification and refinement of the previous method, namely the Stevenson &amp;amp; Porter fuzzy time series method. The Stevenson &amp;amp; Porter method changes the actual data into percentage change which was then developed by Saxena Easo with modifications to the number of data intervals. The data developed by Saxena Easo is dividing the number of intervals into sub-intervals based on the number of frequencies. This research aims to predict the number of Dengue Hemorrhagic Fever cases in Bengkulu Province from the 1st week of 2022 to the 24th week of 2024, and compare it with existing data on the number of cases. Based on the research, the results of the accuracy level of the Mean Absolute Percentage Error (MAPE) in forecasting the number of cases of Dengue Hemorrhagic Fever in Bengkulu Province using the FTS Saxena Easo method obtained a value of 0.359637893 or 3.5%, so the forecast accuracy value obtained is included in the criteria for very good forecasting, because it has an accuracy value below 10%. It can be said that the comparison between actual data and forecasts is not much different.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2025-09-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/45365</dc:identifier>
	<dc:identifier>10.20956/j.v22i1.45365</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 1 (2025): SEPTEMBER 2025; 243-255</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/45365/13344</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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