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

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 calc...

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Main Authors: Tyas Panorama Nan Cerah, Oky Dwi Nurhayati, R. Rizal Isnanto
Format: Article
Language:English
Published: Diponegoro University 2019-01-01
Series:Jurnal Teknologi dan Sistem Komputer
Subjects:
Online Access:https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13145
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author Tyas Panorama Nan Cerah
Oky Dwi Nurhayati
R. Rizal Isnanto
author_facet Tyas Panorama Nan Cerah
Oky Dwi Nurhayati
R. Rizal Isnanto
author_sort Tyas Panorama Nan Cerah
collection DOAJ
description 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.
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spelling doaj.art-6647e734fdd94436ad7a8c04a9cb12812024-03-02T14:51:53ZengDiponegoro UniversityJurnal Teknologi dan Sistem Komputer2338-04032019-01-0171313710.14710/jtsiskom.7.1.2019.31-3712768Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove ForestsTyas Panorama Nan Cerah0Oky Dwi Nurhayati1R. Rizal Isnanto2https://orcid.org/0000-0002-6044-0644Departemen Teknik Komputer, Universitas Diponegoro, IndonesiaDepartemen Teknik Komputer, Universitas Diponegoro, IndonesiaDepartemen Teknik Komputer, Universitas Diponegoro, IndonesiaThis 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.https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13145luas hutan mangrovepengolahan citra digitalk-means clusteringregion growingsegmentasi citra satelit
spellingShingle Tyas Panorama Nan Cerah
Oky Dwi Nurhayati
R. Rizal Isnanto
Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests
Jurnal Teknologi dan Sistem Komputer
luas hutan mangrove
pengolahan citra digital
k-means clustering
region growing
segmentasi citra satelit
title Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests
title_full Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests
title_fullStr Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests
title_full_unstemmed Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests
title_short Comparison of K-Means Clustering and Growing Region Segmentation Methods for Area Measurement of Mangrove Forests
title_sort comparison of k means clustering and growing region segmentation methods for area measurement of mangrove forests
topic luas hutan mangrove
pengolahan citra digital
k-means clustering
region growing
segmentasi citra satelit
url https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13145
work_keys_str_mv AT tyaspanoramanancerah comparisonofkmeansclusteringandgrowingregionsegmentationmethodsforareameasurementofmangroveforests
AT okydwinurhayati comparisonofkmeansclusteringandgrowingregionsegmentationmethodsforareameasurementofmangroveforests
AT rrizalisnanto comparisonofkmeansclusteringandgrowingregionsegmentationmethodsforareameasurementofmangroveforests