Perbandingan Metode Segmentasi K-Means Clustering dan Segmentasi Region Growing untuk Pengukuran Luas Wilayah Hutan Mangrove

Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests

Tyas Panorama Nan Cerah -  Department of Computer Engineering, Universitas Diponegoro, Indonesia
*Oky Dwi Nurhayati -  Department of Computer Engineering, Universitas Diponegoro, Indonesia
R. Rizal Isnanto -  Department of Computer Engineering, Universitas Diponegoro, Indonesia
Received: 15 Nov 2018; Revised: 28 Jan 2019; Accepted: 30 Jan 2019; Published: 31 Jan 2019; Available online: 31 Mar 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
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Abstract
This study aims to examine the k-means clustering and region growing segmentation methods to identify and measure the area of mangrove forests in the Southeast Sulawesi province. The image of the area of this study used Landsat 8 satellite imagery. The area of mangrove forest was carried out by calculating the number of pixels identified as mangrove forests with an area density of 900 m2/pixel. The accuracy of the two segmentation methods in calculating the area was compared based on the same area calculated by LAPAN. The overall accuracy of k-means clustering segmentation method has better accuracy, which is 59.26%, than region growing with 33.33% of accuracy. Both image segmentation methods, k-means clustering and region growing, can be used to calculate the area of mangrove forests in the Southeast Sulawesi region using Landsat 8 satellite imagery.
Keywords
mangrove forest area; digital image processing; k-means clustering; region growing; satellite imagery segmentation

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