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Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform.

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Titel: Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform.
Autoren: Prasetyo, Syukur Toha, Rahman, Fahmi Arief, Suryawati, Sinar, Supriyadi, Slamet, Setiawan, Eko
Quelle: Indonesian Journal of Agricultural Sciences / Jurnal Ilmu Pertanian Indonesia; Oct2025, Vol. 30 Issue 4, p765-772, 8p
Schlagwörter: MACHINE learning, RANDOM forest algorithms, LAND management, GEOGRAPHIC information systems, ENVIRONMENTAL monitoring, GEOSPATIAL data, REMOTE-sensing images, CLOUD computing
Abstract: Land use analysis can provide a foundation for successful and efficient regional planning and environmental monitoring. The application of machine-learning on a cloud computing platform (Google Earth Engine, GEE) in land use analysis enables efficient and rapid processing of spatial data on a wide scale. It overcomes the constraints inherent in conventional approaches. The purpose of this study was to identify land use and estimate its level of accuracy using GEE and a Random Forest machine-learning method. The data utilized were the administrative boundaries of Bangkalan Regency (1:25,000) and Landsat 8 SR L2 C2 T1 satellite images from 2022. Satellite image analysis using the Random Forest algorithm on the GEE platform with the JavaScript API, including masking, cloud masking, class and sampling, training, and testing sample data. Land use study using the Random Forest algorithm yielded the following results in order of area: vegetation 65,040.39 ha (49.98%), agricultural land 31,817.16 ha (24.45%), settlements 20,578.05 ha (15.81%), open land 6,683.94 ha (5.14%), and water bodies 6,021.09 ha (4.63%). The accuracy test in GEE revealed an overall accuracy (OA) of 91.39% and a kappa score of 88.39%, or 0.88. At the same time, validation in the field gave an OA of 88.68% and a Kappa of 85.53%. The findings of this study can be applied to land use evaluation and fundamental decision-making. [ABSTRACT FROM AUTHOR]
Copyright of Indonesian Journal of Agricultural Sciences / Jurnal Ilmu Pertanian Indonesia is the property of IPB University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform.
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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Prasetyo%2C+Syukur+Toha%22">Prasetyo, Syukur Toha</searchLink><br /><searchLink fieldCode="AR" term="%22Rahman%2C+Fahmi+Arief%22">Rahman, Fahmi Arief</searchLink><br /><searchLink fieldCode="AR" term="%22Suryawati%2C+Sinar%22">Suryawati, Sinar</searchLink><br /><searchLink fieldCode="AR" term="%22Supriyadi%2C+Slamet%22">Supriyadi, Slamet</searchLink><br /><searchLink fieldCode="AR" term="%22Setiawan%2C+Eko%22">Setiawan, Eko</searchLink>
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  Label: Source
  Group: Src
  Data: Indonesian Journal of Agricultural Sciences / Jurnal Ilmu Pertanian Indonesia; Oct2025, Vol. 30 Issue 4, p765-772, 8p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22LAND+management%22">LAND management</searchLink><br /><searchLink fieldCode="DE" term="%22GEOGRAPHIC+information+systems%22">GEOGRAPHIC information systems</searchLink><br /><searchLink fieldCode="DE" term="%22ENVIRONMENTAL+monitoring%22">ENVIRONMENTAL monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22GEOSPATIAL+data%22">GEOSPATIAL data</searchLink><br /><searchLink fieldCode="DE" term="%22REMOTE-sensing+images%22">REMOTE-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22CLOUD+computing%22">CLOUD computing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Land use analysis can provide a foundation for successful and efficient regional planning and environmental monitoring. The application of machine-learning on a cloud computing platform (Google Earth Engine, GEE) in land use analysis enables efficient and rapid processing of spatial data on a wide scale. It overcomes the constraints inherent in conventional approaches. The purpose of this study was to identify land use and estimate its level of accuracy using GEE and a Random Forest machine-learning method. The data utilized were the administrative boundaries of Bangkalan Regency (1:25,000) and Landsat 8 SR L2 C2 T1 satellite images from 2022. Satellite image analysis using the Random Forest algorithm on the GEE platform with the JavaScript API, including masking, cloud masking, class and sampling, training, and testing sample data. Land use study using the Random Forest algorithm yielded the following results in order of area: vegetation 65,040.39 ha (49.98%), agricultural land 31,817.16 ha (24.45%), settlements 20,578.05 ha (15.81%), open land 6,683.94 ha (5.14%), and water bodies 6,021.09 ha (4.63%). The accuracy test in GEE revealed an overall accuracy (OA) of 91.39% and a kappa score of 88.39%, or 0.88. At the same time, validation in the field gave an OA of 88.68% and a Kappa of 85.53%. The findings of this study can be applied to land use evaluation and fundamental decision-making. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Indonesian Journal of Agricultural Sciences / Jurnal Ilmu Pertanian Indonesia is the property of IPB University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.18343/jipi.30.4.765
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 765
    Subjects:
      – SubjectFull: MACHINE learning
        Type: general
      – SubjectFull: RANDOM forest algorithms
        Type: general
      – SubjectFull: LAND management
        Type: general
      – SubjectFull: GEOGRAPHIC information systems
        Type: general
      – SubjectFull: ENVIRONMENTAL monitoring
        Type: general
      – SubjectFull: GEOSPATIAL data
        Type: general
      – SubjectFull: REMOTE-sensing images
        Type: general
      – SubjectFull: CLOUD computing
        Type: general
    Titles:
      – TitleFull: Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Prasetyo, Syukur Toha
      – PersonEntity:
          Name:
            NameFull: Rahman, Fahmi Arief
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            NameFull: Suryawati, Sinar
      – PersonEntity:
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            NameFull: Supriyadi, Slamet
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            NameFull: Setiawan, Eko
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          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 08534217
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            – Type: volume
              Value: 30
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Indonesian Journal of Agricultural Sciences / Jurnal Ilmu Pertanian Indonesia
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