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All Journal Jurnal Ilmu Pertanian Indonesia JTSL (Jurnal Tanah dan Sumberdaya Lahan)
Prasetyo, Syukur Toha
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Agriculture, Biological Sciences & Forestry Environmental Science

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Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform Prasetyo, Syukur Toha; Rahman, Fahmi Arief; Suryawati, Sinar; Supriyadi, Slamet; Setiawan, Eko
Jurnal Ilmu Pertanian Indonesia Vol. 30 No. 4 (2025): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18343/jipi.30.4.765

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. Keywords: land use, random forest, geographic information system, remote sensing
HUBUNGAN INDEKS VEGETASI DENGAN KLOROFIL DAN NITROGEN PADA DAUN TANAMAN JAGUNG BERBASIS CLOUD COMPUTING PLATFORM Rahman, Fahmi Arief; Aini, Yuli Kurrotul Binti; Prasetyo, Syukur Toha; Suhartono; Suryawati, Sinar
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 1 (2026)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.1.14

Abstract

Vegetation indices are algorithms that represent aspects of vegetation such as leaf area index, biomass, and chlorophyll concentration. The index is thought to be used to determine chlorophyll and nitrogen content which makes it more effective and efficient. The purpose of this study is to determine the relationship between vegetation indices and chlorophyll and nitrogen content of corn leaves through a cloud computing platform. Vegetation indices were obtained from Sentinel 2A image processing with Google Earth Engine. Chlorophyll and nitrogen content were obtained from laboratory analysis. This study used 13 sample points in corn crop areas spread across Dlanggu, Bangsal, and Jatirejo sub-districts in Mojokerto district. The correlation test was used to determine the level of relationship between variables. The results showed that the vegetation index generated from image processing through GEE had no correlation with chlorophyll and nitrogen content from laboratory analysis. The weak correlation was thought to be due to atmospheric effects such as clouds, as well as climatic effects such as the rainy season and cloudy skies, which cause the calculation of vegetation indices to be disrupted. The dominant phase of the sample plants in the early generative phase and the possibility of errors in the analysis of chlorophyll and nitrogen content were thought to contribute to the lack of correlation in this study.
Co-Authors Aini, Yuli Kurrotul Binti EKO SETIAWAN Fahmi Arief Rahman Sinar Suryawati Slamet Supriyadi Suhartono