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Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Forum Geografi · Vol. 37 No. 1 (2023)

DOI
10.23917/forgeo.v37i1.15248
ISSN
2460-3945, 0852-0682
Terbit
2023-08-08
Penerbit
Universitas Muhammadiyah Surakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc-nd/4.0

Abstrak

Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796.

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22 penulis · 43 tautan kepenulisan bersama · 6 lintas organisasi

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  • Universitas Gadjah Mada
  • Muhammadiyah University of Yogyakarta
  • Diamond Harbour Women's University
  • Indonesia University of Education
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Rekan penulisOrganisasiArtikel bersama
Projo DanoedoroUniversitas Gadjah Mada2
I Kade Alfian Kusuma WirayudaUniversitas Gadjah Mada1
Andung Bayu SekaranomUniversitas Gadjah Mada1
Sudaryatno SudaryatnoUniversitas Gadjah Mada1
Bagus WiratmokoUniversitas Gadjah Mada1
Totok Wahyu WibowoUniversitas Gadjah Mada1
Wahyu NurbandiUniversitas Gadjah Mada1
Muhammad KamalUniversitas Gadjah Mada1
Muhammad Sufwandika WijayaUniversitas Gadjah Mada1
Sanjiwana ArjasakusumaUniversitas Gadjah Mada1
Totok GunawanUniversitas Gadjah Mada1
Sugeng Juwono MardihusodoUniversitas Gadjah Mada1
Kusbaryanto KusbaryantoMuhammadiyah University of Yogyakarta1
Tri Wulandari KesetyaningsihMuhammadiyah University of Yogyakarta1
Rahmat Azul MizanUniversitas Gadjah Mada1
Nur Mohammad FardaUniversitas Gadjah Mada1
Ni Kadek Oki FebriantiUniversitas Gadjah Mada1
Muhammad ArrafiIndonesia University of Education1

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Sumber metadata: OAI-PMH (metadata jurnal) http://journals.ums.ac.id/index.php/fg/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">Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images</dc:title>
	<dc:creator>Widayani, Prima</dc:creator>
	<dc:creator>Fadilah, Achmad</dc:creator>
	<dc:creator>Irawan, Irfan Zaki</dc:creator>
	<dc:creator>Ghosh, Kapil</dc:creator>
	<dc:subject xml:lang="en-US">Geography;Remote Sensing;Geography Information Science</dc:subject>
	<dc:subject xml:lang="en-US">Slum, Machine Learning, Support Vector Machine</dc:subject>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:description xml:lang="en-US">Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Surakarta</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-08-08</dc:date>
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	<dc:identifier>https://journals.ums.ac.id/index.php/fg/article/view/15248</dc:identifier>
	<dc:identifier>10.23917/forgeo.v37i1.15248</dc:identifier>
	<dc:source xml:lang="en-US">Forum Geografi; Vol 37, No 1 (2023): July 2023</dc:source>
	<dc:source>2460-3945</dc:source>
	<dc:source>0852-0682</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.ums.ac.id/index.php/fg/article/view/15248/8662</dc:relation>
	<dc:relation>https://journals.ums.ac.id/index.php/fg/article/view/15248/8935</dc:relation>
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	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Prima Widayani</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc-nd/4.0</dc:rights>
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