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ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS

Jurnal Geografi · Vol. 13 No. 2 (2021) · pp. 211–224

DOI
10.24114/jg.v13i2.21019
ISSN
2549-7057, 2085-8167
Published
2021-07-21
Publisher
Universitas Negeri Medan
Language
en

Abstract

English

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image

English

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image

Collaboration network of the authors

27 authors · 59 co-authorship links

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

  • Universitas Gadjah Mada
  • Muhammadiyah University of Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Projo DanoedoroUniversitas Gadjah Mada2
Totok Wahyu WibowoAffiliation unknown2
Muhammad KamalAffiliation unknown2
Nurul KhakhimAffiliation unknown1
Arief WicaksonoAffiliation unknown1
Hartono HartonoAffiliation unknown1
Barano SS MeterayAffiliation unknown1
Sudaryatno SudaryatnoAffiliation unknown1
Bagus WiratmokoAffiliation unknown1
Wahyu NurbandiAffiliation unknown1
Sanjiwana ArjasakusumaAffiliation unknown1
Muhammad Sufwandika WijayaAffiliation unknown1
R RijantaAffiliation unknown1
Rini RachmawatiAffiliation unknown1
Sara Dwi KumiartoAffiliation unknown1
Totok GunawanAffiliation unknown1
Sugeng Juwono MardihusodoAffiliation unknown1
Kusbaryanto KusbaryantoMuhammadiyah University of Yogyakarta1
Tri Wulandari KesetyaningsihAffiliation unknown1
Achmad FadilahAffiliation unknown1
Irfan Zaki IrawanAffiliation unknown1
Kapil GhoshAffiliation unknown1
Ni Kadek Oki FebriantiAffiliation unknown1
Muhammad ArrafiAffiliation 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) http://jurnal.unimed.ac.id/2012/index.php/geo/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS</dc:title>
	<dc:title xml:lang="en-US">ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS</dc:title>
	<dc:creator xml:lang="en">Mizan, Rahmat Azul</dc:creator>
	<dc:creator xml:lang="en">Widayani, Prima</dc:creator>
	<dc:creator xml:lang="en">Farda, Nur Mohammad</dc:creator>
	<dc:description xml:lang="en">The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image</dc:description>
	<dc:description xml:lang="en-US">The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Medan</dc:publisher>
	<dc:date>2021-07-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/21019</dc:identifier>
	<dc:identifier>10.24114/jg.v13i2.21019</dc:identifier>
	<dc:source xml:lang="en">JURNAL GEOGRAFI; Vol. 13 No. 2 (2021): JURNAL GEOGRAFI; 211-224</dc:source>
	<dc:source>2549-7057</dc:source>
	<dc:source>2085-8167</dc:source>
	<dc:source>10.24114/jg.v13i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/21019/16508</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 JURNAL GEOGRAFI</dc:rights>
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