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All Journal Jurnal Pendidikan Geografi Undiksha JTSL (Jurnal Tanah dan Sumberdaya Lahan)
Prabandari, Amanah Anggun
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Agriculture, Biological Sciences & Forestry Education Environmental Science

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ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR) Prabandari, Amanah Anggun; Manessa, Masita Dwi Mandini
Jurnal Tanah dan Sumberdaya Lahan Vol. 11 No. 2 (2024)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2024.011.2.11

Abstract

Monitoring the development of built-up areas can be done by observing remote sensing time series data such as Satellite Imagery. Google Earth Engine (GEE) makes it easy for users to access satellite image data, data processing and data analysis. GEE provides various machine learning algorithms to extract land cover data. This research aims to analyze the development of built-up areas using time series of remote sensing data, namely Sentinel 2A images recorded in 2019 and 2023 and comparing Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Gradient Tree Boost (GTB) algorithms and predicts built-up areas in 2027. Based on the results of this research, RF is the algorithm with the highest accuracy in mapping land cover in Ciputri Village with an Overall Accuracy (OA) of 92% and a Kappa Coefficent (KC) of 0.89 in both the 2019 and 2023 classification results, while the lowest accuracy is the SVM algorithm. A comparison of the built-up land area between the 2019 and 2023 classification results shows a decrease in the built-up land area of 3.08 ha. Meanwhile, the prediction results for 2027 show an increase in built-up areas to 114.72 ha.
Pembuatan Peta Kemiringan Lereng untuk Analisis Kesesuaian Kawasan Permukiman di Kabupaten Purbalingga Prabandari, Amanah Anggun; Wibowo , Adi
Jurnal Pendidikan Geografi Undiksha Vol. 12 No. 2 (2024): Jurnal Pendidikan Geografi Undiksha
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpg.v12i02.70078

Abstract

Permukiman merupakan jenis penggunaan lahan yang paling dominan dan dinamis karena didalamnya memuat segala bentuk aktivitas manusia. Untuk membatasi perkembangan kawasan permukiman agar sesuai dengan peruntukannya dapat digunakan analisis kesesuaian lahan seperti metode Spatial Multi Criteria Analysis (SMCA). Salah satu variabel yang digunakan dalam metode SMCA adalah peta kemiringan lereng. Peta kemiringan lereng dapat diperoleh melalui pengolahan data DEM. Tujuan dari penelitian ini adalah untuk menganalisis kesesuaian kawasan permukiman di Kabupaten Purbalingga berdasarkan variabel kemiringan lereng yang selanjutnya digunakan untuk mengevaluasi peruntukan kawasan permukiman pada RTRW Kabupaten Purbalingga. Kemiringan lereng di Kabupaten Purbalingga dibagi dalam 5 kelas lereng yaitu 0-8% (datar), 8-15% (landai), 15-25% (agak curam), 25-45 % (curam) dan >45% (sangat curam) dengan persentase luas lereng  masing-masing kelas berturut-turut 26,37%, 24,84%, 20,68%, 16,61% dan 11,51%. Total luas wilayah yang dapat dikembangkan sebagai kawasan permukiman di Kabupaten Purbalingga sebesar 57.826,25 Ha atau 71,89% dari total luas wilayah. Hasil evaluasi RTRW Kabupaten Purbalingga berdasarkan hasil analisis kesesuaian lahan kawasan permukiman menunjukkan bahwa hampir seluruh alokasi pola ruang kawasan permukiman berada pada kategori dapat dikembangkan sebagai kawasan permukiman meskipun terdapat sebagian kecil yang berada pada kelas sangat tidak sesuai terutama dibagian utara wilayah Kabupaten Purbalingga.
ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR) Prabandari, Amanah Anggun; Manessa, Masita Dwi Mandini
Jurnal Tanah dan Sumberdaya Lahan Vol. 11 No. 2 (2024)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2024.011.2.11

Abstract

Monitoring the development of built-up areas can be done by observing remote sensing time series data such as Satellite Imagery. Google Earth Engine (GEE) makes it easy for users to access satellite image data, data processing and data analysis. GEE provides various machine learning algorithms to extract land cover data. This research aims to analyze the development of built-up areas using time series of remote sensing data, namely Sentinel 2A images recorded in 2019 and 2023 and comparing Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Gradient Tree Boost (GTB) algorithms and predicts built-up areas in 2027. Based on the results of this research, RF is the algorithm with the highest accuracy in mapping land cover in Ciputri Village with an Overall Accuracy (OA) of 92% and a Kappa Coefficent (KC) of 0.89 in both the 2019 and 2023 classification results, while the lowest accuracy is the SVM algorithm. A comparison of the built-up land area between the 2019 and 2023 classification results shows a decrease in the built-up land area of 3.08 ha. Meanwhile, the prediction results for 2027 show an increase in built-up areas to 114.72 ha.
Co-Authors Adi Wibowo MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI