DOI: https://doi.org/10.14710/jtsiskom.7.1.2019.31-37

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

Department of Computer Engineering, Universitas Diponegoro, Indonesia

Received: 15 Nov 2018; Revised: 28 Jan 2019; Accepted: 30 Jan 2019; Available online: 31 Mar 2019; Published: 31 Jan 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.
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Keywords: mangrove forest area; digital image processing; k-means clustering; region growing; satellite imagery segmentation
Funding: Department of Computer Engineering, Universitas Diponegoro

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Section: Original Research Articles
Language : ID
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  1. I. Majid, H. H. I. Al Muhdar, R. Fachur, and I. Syamsuri, “Konservasi Hutan Mangrove di Pesisir Pantai Kota Ternate Terintegrasi dengan Kurikulum Sekolah,” Bioedukasi, vol. 4, no. 2, pp.488-496, 2016
  2. M. Vitasari, “Kerentanan Ekosistem Mangrove Terhadap Ancaman Gelombang Ektrim/Abrasi di Kawasan Konservasi Pulau Dua Banten,” Bioedukasi: Jurnal Pendidikan Biologi, vol. 8, no. 2, pp. 33-36, 2015
  3. Pusat Pemanfaatan Pengindraan Jauh, Pedoman Teknik Pengolahan Data Pengindraan Jauh Landsat 8 untuk Mangrove. Lembaga Penerbangan dan Antariksa Nasional, 2015
  4. E. T. Opa, “Analisis Perubahan Lahan Mangrove di Kabupaten Pohuwato Propinsi Gorontalo dengan Menggunakan Citra Landsat,” Jurnal Perikanan dan Kelautan Tropis, vol. 6, no. 2, pp. 79-82, 2010
  5. A. K. S. Manoppo, N. Anggraini, and Y. Marini, “Identifikasi Mangrove dengan Metode Optimum Index Factor (OIF) pada Data SPOT6 dan Landsat 8 di Pulau Lingayan,” in Pertemuan Ilmiah Tahunan XX dan Kongres VI MAPIN: Masyarakat Ahli Penginderaan Jauh, Bogor, Indonesia, Feb. 2015
  6. K. T. Setiawan, N. Aggraini, and A. K. S. Manoppo, “Estimasi Perhitungan Luas Daerah di Pulau-Pulau Kecil Menggunakan data Citra Satelit Landsat 8 Studi Kasus: Pulau Pramuka Kepulauan Seribu DKI Jakarta,” in Seminar Nasional Pengindraan Jauh, Indonesia, 2016, pp. 294-300
  7. D. Heksaputra, P. W. Dhina, and S. Nilawati, “Perbaikan Kualitas Citra Iris Mata untuk Pengenalan Pola (Biometric),” Khazanah: Jurnal Mahasiswa, vol. 7, no. 2, pp. 11-23, 2015
  8. I. A. Kesuma, H. Herman, and M. Munawir, “Penerapan Metode Klaster K-Means pada Segmentasi Warna Citra Digital dengan Pemrograman R”, in Seminar Nasional Inovasi dan Teknologi Informasi, Medan, Indonesia, Nov. 2016, pp. 427-430
  9. A. Atina, “Segmentasi Citra Paru Menggunakan Metode K-Means Clustering,” Jurnal Pendidikan Fisika dan Keilmuan, vol. 3, no. 2, pp. 57-65, 2017
  10. F. Kanditami, D. Saepudin, and A. Rizal, “Analisis Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Region Growing dalam Deteksi Gejala Kanker Payudara pada Citra Mammogram,” Jurnal Elektro Unika Atma Jaya, vol. 7, no. 1, pp. 15-28, 2014
  11. D. P. Agrisativa, “Estimasi Bobot Karkas Sapi Berdasarkan Metode Region Growing dan Klasifikasi K-NN,” Skripsi, Universitas Telkom, Bandung, 2018
  12. Pusat Pemanfaatan Penginderaan Jauh LAPAN, “Sebaran Perubahan Hutan Mangrove Indonesia Tahun 2016,” Oct. 2017. [Online]. Available: http://spbn.pusfatja.lapan.go.id/layers/geonode%3Amangrove_2014_2016_penyusutan2#info
  13. E. Prasetyo, Pengolahan Citra Digital dan Aplikasinya Menggunakan MATLAB. Yogyakarta: Penerbit Andi, 2011

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