Komparasi Metode Klasifikasi Tersupervisi untuk Pemetaan Lahan Terbangun dan NonTerbangun Menggunakan Landsat 8 OLI dan Google Earth Engine (Studi Kasus: Kota Malang)

Riswan Septriayadi Sianturi

Abstract


Pertumbuhan kota Malang telah berdampak pada peningkatan lahan terbangun dan pengurangan lahan nonterbangun. Dinamika lahan terbangun dan nonterbangun tersebut mempengaruhi interaksi manusia dengan lingkungan dan keadaan ekonomi dan sosial masyarakat kota Malang. Oleh karena itu, informasi distribusi dan luasan lahan terbangun dan nonterbangun diperlukan untuk dapat membantu pengambilan berbagai keputusan perencanaan kota dan wilayah. Sayangnya, belum banyak penelitian terkait distribusi lahan terbangun dan nonterbangun di kota Malang. Penelitian ini bertujuan untuk memetakan distribusi lahan terbangun dan nonterbangun dengan Landsat 8 OLI dan Google Earth Engine (GEE) di kota Malang. Indeks spektral yang dihasilkan dari Landsat 8 OLI seperti NDVI, EVI, BU (NDBI-NDVI), LSWI, dan MNDWI serta titik ketinggian (elevation) dan tingkat kelerengan (slope) yang diturunkan dari ALOS DSM 30m digunakan sebagai masukan untuk klasifikasi tersupervisi. Ragam teknik klasifikasi tersupervisi seperti Support Vector Machine (SVM), Random Forest (RF), Minimum Distance (MD), Gradient Tree Boost (GTB), dan Classification And Regression Tree (CART) digunakan untuk memetakan distribusi lahan terbangun dan nonterbangun. Random Forest menunjukkan akurasi tertinggi dalam memetakan lahan terbangun dan nonterbangun di kota Malang dengan Overall Accuracy 0,96 dan Kappa Coefficient 0,91. Luas area terbangun diestimasi sebesar ~55%-61% dan nonterbangun sebesar ~39%-45% dari total luas kota Malang.

Keywords


Google Earth Engine, lahan terbangun, lahan nonterbangun, landsat 8 OLI, kota Malang

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References


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DOI: http://dx.doi.org/10.12962/j2716179X.v17i2.13434

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