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Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method

IAES International Journal of Artificial Intelligence · Vol. 12 No. 1 (2023) · hlm. 496–504

DOI
10.11591/ijai.v12.i1.pp496-504
ISSN
2252-8938, 2089-4872
Terbit
2023-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Dengue fever is still a crucial public health problem in Indonesia, with the highest case fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-Net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38.

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  • Institut Teknologi Sepuluh Nopember
  • Islamic University of Indonesia
  • Bandung Institute of Technology
  • Universitas Muhammadiyah Malang
  • Universitas Katolik Widya Mandira
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Edwin RiksakomaraInstitut Teknologi Sepuluh Nopember7
I Gusti Agung PremanandaInstitut Teknologi Sepuluh Nopember4
Muhammad Reza PahlawanInstitut Teknologi Sepuluh Nopember4
Erma SuryaniInstitut Teknologi Sepuluh Nopember1
Habibah Asma'ul HusnaAfiliasi tidak diketahui1
Leivina Kartika DewiAfiliasi tidak diketahui1
Handayani TjandrasaAfiliasi tidak diketahui1
Anang FirdausInstitut Teknologi Sepuluh Nopember1
Vicha Azthanty SupoyoInstitut Teknologi Sepuluh Nopember1
Febriliyan SamopaInstitut Teknologi Sepuluh Nopember1
Radityo Prasetianto WibowoInstitut Teknologi Sepuluh Nopember1
Tresnaning ArifiyahInstitut Teknologi Sepuluh Nopember1
Pujiadi PujiadiAfiliasi tidak diketahui1
Siti Aminatus ZehrohInstitut Teknologi Sepuluh Nopember1
Nur Aini LestariInstitut Teknologi Sepuluh Nopember1
Hendra SetiawanIslamic University of Indonesia1
Alvin SahroniIslamic University of Indonesia1
Hasballah ZakariaBandung Institute of Technology1
Yeyen SudiartiInstitut Teknologi Sepuluh Nopember1
Muhammad Ilham PerdanaUniversitas Muhammadiyah Malang1
Adri Gabriel SooaiUniversitas Katolik Widya Mandira1
Shof Rijal Ahlan RobbaniInstitut Teknologi Sepuluh Nopember1
Yusnardo TendioInstitut Teknologi Sepuluh Nopember1
Helena Angelita DepariInstitut Teknologi Sepuluh Nopember1
Muhammad Arif NurimanInstitut Teknologi Sepuluh Nopember1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method</dc:title>
	<dc:creator>Nabilah, Mursyidatun</dc:creator>
	<dc:creator>Tyasnurita, Raras</dc:creator>
	<dc:creator>Mahananto, Faizal</dc:creator>
	<dc:creator>Anggraeni, Wiwik</dc:creator>
	<dc:creator>Vinarti, Retno Aulia</dc:creator>
	<dc:creator>Muklason, Ahmad</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Dengue fever; Dengue forecast; Ensemble forecasting;</dc:subject>
	<dc:description xml:lang="en-US">Dengue fever is still a crucial public health problem in Indonesia, with the highest case fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-Net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. </dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-03-01</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21070</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i1.pp496-504</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 1: March 2023; 496-504</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21070/13572</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>