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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) · hlm. 211–224

DOI
10.24114/jg.v13i2.21019
ISSN
2549-7057, 2085-8167
Terbit
2021-07-21
Penerbit
Universitas Negeri Medan
Bahasa
en

Abstrak

Inggris

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

Inggris

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

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  • Universitas Gadjah Mada
  • Muhammadiyah University of Yogyakarta
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Rekan penulisOrganisasiArtikel bersama
Projo DanoedoroUniversitas Gadjah Mada2
Totok Wahyu WibowoAfiliasi tidak diketahui2
Muhammad KamalAfiliasi tidak diketahui2
Nurul KhakhimAfiliasi tidak diketahui1
Arief WicaksonoAfiliasi tidak diketahui1
Hartono HartonoAfiliasi tidak diketahui1
Barano SS MeterayAfiliasi tidak diketahui1
Sudaryatno SudaryatnoAfiliasi tidak diketahui1
Bagus WiratmokoAfiliasi tidak diketahui1
Wahyu NurbandiAfiliasi tidak diketahui1
Sanjiwana ArjasakusumaAfiliasi tidak diketahui1
Muhammad Sufwandika WijayaAfiliasi tidak diketahui1
R RijantaAfiliasi tidak diketahui1
Rini RachmawatiAfiliasi tidak diketahui1
Sara Dwi KumiartoAfiliasi tidak diketahui1
Totok GunawanAfiliasi tidak diketahui1
Sugeng Juwono MardihusodoAfiliasi tidak diketahui1
Kusbaryanto KusbaryantoMuhammadiyah University of Yogyakarta1
Tri Wulandari KesetyaningsihAfiliasi tidak diketahui1
Achmad FadilahAfiliasi tidak diketahui1
Irfan Zaki IrawanAfiliasi tidak diketahui1
Kapil GhoshAfiliasi tidak diketahui1
Ni Kadek Oki FebriantiAfiliasi tidak diketahui1
Muhammad ArrafiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.unimed.ac.id/2012/index.php/geo/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<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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