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Tyas Panorama Nan Cerah
Department of Computer Engineering, Universitas Diponegoro
Computer Science & IT Electrical & Electronics Engineering

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Perbandingan Metode Segmentasi K-Means Clustering dan Segmentasi Region Growing untuk Pengukuran Luas Wilayah Hutan Mangrove Tyas Panorama Nan Cerah; Oky Dwi Nurhayati; R. Rizal Isnanto
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 1, Year 2019 (January 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (226.824 KB) | DOI: 10.14710/jtsiskom.7.1.2019.31-37

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.
Co-Authors Oky Dwi Nurhayati R Rizal Isnanto