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Spatial decision tree model for garlic land suitability evaluation

IAES International Journal of Artificial Intelligence · Vol. 10 No. 3 (2021) · hlm. 666–675

DOI
10.11591/ijai.v10.i3.pp666-675
ISSN
2252-8938, 2089-4872
Terbit
2021-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.

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  • IPB University
  • National Research and Innovation Agency
  • Universitas Gadjah Mada
  • Sumatera Institute of Technology
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Rekan penulisOrganisasiArtikel bersama
Lailan SyaufinaIPB University5
Wisnu Ananta KusumaIPB University4
Luki AbdullahIPB University3
Irman HermadiIPB University2
Kahfi Heryandi SuradiradjaIPB University2
Shelvie Nidya NeymanIPB University2
Syarifah Iis AisyahIPB University2
Suryo WiyonoIPB University2
Sony Hartono WijayaIPB University2
Alusyanti PrimawatiIPB University2
Dewi Apri AstutiIPB University2
Risa Intan KomaraasihIPB University2
Muhammad Asyhar AgmalaroIPB University2
Larasati LarasatiUniversitas Gadjah Mada1
Nabila Sekar RamadhantiIPB University1
Global Ilham SampurnoIPB University1
Zuliar EfendiIPB University1
Yoga HerawanIPB University1
Ahmad Bintang ArifIPB University1
Hari Agung AdriantoIPB University1
Assad HidayatIPB University1
Dody HaryadiAfiliasi tidak diketahui1
Budi SetiawanAfiliasi tidak diketahui1
Helda Yunita TaihuttuIPB University1
Abdul MuftiIPB University1
Olivia YofanandaIPB University1
Christofora Hanny WijayaIPB University1
Hanifah Nuryani LioeIPB University1
Arya Widura RitongaIPB University1
Tiffani Nindya ArisantiIPB University1
Rumaisha Afifatul HafizahIPB University1
Djoko TamamiNational Research and Innovation Agency1
Ika RoostikaNational Research and Innovation Agency1
Nalar IstiqomahIPB University1
Rizka FatrianiSumatera Institute of Technology1
Muhammad Fahrury RomdendineIPB University1

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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">Spatial decision tree model for garlic land suitability evaluation</dc:title>
	<dc:creator>Nurkholis, Andi</dc:creator>
	<dc:creator>Sitanggang, Imas Sukaesih</dc:creator>
	<dc:creator>Annisa, Annisa</dc:creator>
	<dc:creator>Sobir, Sobir</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Garlic; Land suitability; Spatial decision tree; Spatial join relation</dc:subject>
	<dc:description xml:lang="en-US">Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.</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>2021-09-01</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/20702</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i3.pp666-675</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 3: September 2021; 666-675</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20702/13213</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 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>