Perbandingan Parameter Omn dan RGO dalam Segmentasi OBIA untuk Klasifikasi Penutupan/Penggunaan Lahan Menggunakan K-Nearest Neighbor di Kabupaten Sumedang
DOI:
https://doi.org/10.29244/jitl.28.1.1-10Keywords:
object based classification, scale, shape, compactness parametersAbstract
Informasi penutupan/penggunaan lahan sangat penting untuk analisis lingkungan, namun akurasinya bergantung pada metode segmentasi atau klasifikasi. Penelitian ini bertujuan untuk menganalisis perbedaan hasil segmentasi dan akurasi klasifikasi dengan menggunakan Original Multiresolution (OMN) dan Region Grow on Object (RGO) yang diintegrasikan dengan metode K-Nearest Neighbor (K-NN) di Kabupaten Sumedang. Pendekatan Object-Based Image Analysis (OBIA) diterapkan dengan variasi parameter skala, bentuk, dan kekompakan, serta akurasi dinilai menggunakan Overall Accuracy (OA), User’s Accuracy (UA), dan Producer’s Accuracy (PA). Hasil penelitian menunjukkan bahwa OMN sensitif terhadap parameter skala dan cenderung menghasilkan over-segmentation, sedangkan RGO menghasilkan objek yang lebih besar, homogen, dan stabil. Klasifikasi berbasis RGO menunjukkan kinerja yang lebih konsisten dengan OA sedikit lebih tinggi, khususnya pada kelas homogen seperti hutan dan badan air. Sebaliknya, OMN mampu menangkap detail spasial yang lebih halus namun rentan terhadap fragmentasi. Oleh karena itu, RGO dengan parameter moderat (skala 0,5; bentuk 0,1; dan kekompakan 0,4) dianggap sebagai konfigurasi optimal. Namun, ketidakpastian masih terjadi pada kelas dengan kemiripan spektral tinggi, sehingga diperlukan metode klasifikasi yang lebih adaptif atau data resolusi lebih tinggi pada penelitian selanjutnya.
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Department of Soil Science and Land Resources Departemen Ilmu Tanah dan Sumberdaya Lahan, Faculty of Agriculture Fakultas Pertanian, IPB University
