Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Tanah dan Lingkungan
Muhammad Ardiansyah
Departemen Ilmu Tanah dan Sumberdaya Lahan, Fakultas Pertanian, IPB Uninersity
Agriculture, Biological Sciences & Forestry Environmental Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparison of OMN and RGO Parameters in OBIA Segmentation for Land Cover and Land Use Classification Using Random Forest in Sumedang Regency Muhammad Ardiansyah; Boedi Tjahjono; Niken Dwia Oktaviani
Jurnal Ilmu Tanah dan Lingkungan Vol 28 No 1 (2026): Jurnal Ilmu Tanah dan Lingkungan
Publisher : Departemen Ilmu Tanah dan Sumberdaya Lahan, Fakultas Pertanian, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitl.28.1.1-10

Abstract

Land use/land cover (LULC) information is essential for environmental analysis, yet its accuracy depends on segmentation or classification methods. This study aims to analyze differences in segmentation results and classification accuracy using Original Multiresolution (OMN) and Region Grow on Object (RGO) integrated with the K-Nearest Neighbor (K-NN) method in Sumedang Regency. An Object-Based Image Analysis (OBIA) approach was applied with variations in scale, shape, and compactness, and accuracy was assessed using Overall Accuracy (OA), User’s Accuracy (UA), and Producer’s Accuracy (PA). The results show that OMN is sensitive to the scale parameter and tends to produce over-segmentation, while RGO generates larger, more homogeneous, and more stable objects. RGO-based classification achieved more consistent performance with slightly higher then OA, particularly for homogeneous classes such as forest and water bodies. In contrast, OMN captures finer spatial details but is prone to fragmentation. Therefore, RGO with moderate parameters (scale 0.5; shape 0.1 and compactness 0.4) is considered the optimal configuration. However, uncertainties remain in spectrally similar classes, suggesting the need for more adaptive classification methods or higher-resolution data in future studies.
Co-Authors Boedi Tjahjono Niken Dwia Oktaviani