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All Journal Indonesian Journal of Agriculture and Environmental Analytics
Syaeful Rahmat
Hasanuddin University
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Environmental Science Health Professions Physics

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Comparasing Landuse/Landcover Classification Using Pixel and Object Based Random Forest and SVM Algorithm on Sentinel-2 Imagery Over Enrekang Region Nurfadila JS; Rismaneswati Rismaneswati; Syaeful Rahmat
Indonesian Journal of Agriculture and Environmental Analytics Vol. 5 No. 2 (2026): July 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijaea.v5i2.16889

Abstract

Land cover and Land use maps are base for monitoring, understanding and evaluating physical and socio-economic characteristics in a region. This study uses sentinel 2b level 2a as an material for analysis. Classification using Pixel and Object Based Random Forest and SVM Algorithm on Sentinel-2 Immagery over Enrekang Region. Confusion matrix were then performed using visually interpreted Pan-sharpened and orthorectified SPOT-6/7 imagery to calculate the accuracy. Overall accuracy shows that classification using objects based Random Forest or SVM Algorithm is greater than classification using pixels based Random Forest or SVM Algorithm. Overall accuracy of the random forest algorithm increases by 1-3% from the SVM algorithm on pixel or object classification.
Co-Authors Nurfadila JS Rismaneswati Rismaneswati