KOMPARASI KLASIFIKASI TUTUPAN LAMUN BERDASARKAN ALGORITMA SVM DAN FUZZY MENGGUNAKAN CITRA MULTI-SKALA DI PULAU KODINGARENG LOMPO

Jurnal Ilmu dan Teknologi Kelautan Tropis

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Title KOMPARASI KLASIFIKASI TUTUPAN LAMUN BERDASARKAN ALGORITMA SVM DAN FUZZY MENGGUNAKAN CITRA MULTI-SKALA DI PULAU KODINGARENG LOMPO
COMPARISON OF SEAGRASS COVER CLASSIFICATION BASED-ON SVM AND FUZZY ALGORITHMS USING MULTI-SCALE IMAGERY IN KODINGARENG LOMPO ISLAND
 
Creator Sabilah, Anisa Aulia
Siregar, Vincentius Paulus
Amran, Muhammad Anshar
 
Subject accuracy
mapping
seagrass condition
sentinel-2
worldview-2
akurasi
kondisi lamun
pemetaan
sentinel-2
worldview-2
 
Description Padang lamun mempunyai peranan ekologi bagi lingkungan laut dangkal yaitu sebagai habitat biota, produsen primer, penangkap sedimen serta berperan sebagai pendaur zat-zat hara. Mengingat pentingnya peranan ekosistem padang lamun maka kelestarian sumber daya alam ini perlu dijaga, oleh karena itu pemetaan dan pemantauan yang terus-menerus terhadap keberadaan padang lamun sangat penting dilakukan. Metode penginderaan jauh merupakan metode yang dapat digunakan untuk memetakan dan memantau kondisi padang lamun. Perkembangan teknologi sensor satelit yang pesat saat ini, khususnya resolusi spasial dan spektral sensor meningkatkan kualitas peta sebaran lamun. Penggunaan metode dan skema klasifikasi yang kurang tepat dalam klasifikasi kondisi lamun dari citra satelit juga termasuk hal yang dapat memengaruhi akurasi peta, sehingga dibutuhkan berbagai alternatif kajian algoritma yang digunakan. Pada penelitian ini digunakan algoritma Support Vector Machine dan Logika Fuzzy menggunakan citra satelit WorldView-2 dan Sentinel-2 di Pulau Kodingareng Lompo dengan empat kelas tutupan lamun yaitu jarang (0-25%), sedang (26-50%), padat (51-75%), dan sangat padat (76-100%). Hasil yang diperoleh adalah algoritma Logika Fuzzy menggunakan citra WorldView-2 memiliki akurasi keseluruhan klasifikasi tutupan lamun yang paling baik sebesar 78,60%.
Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the Support Vector Machine and Fuzzy Logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imageries on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the Fuzzy Logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification.
 
Publisher Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University
 
Date 2021-04-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://journal.ipb.ac.id/index.php/jurnalikt/article/view/34765
10.29244/jitkt.v13i1.34765
 
Source Jurnal Ilmu dan Teknologi Kelautan Tropis; Vol. 13 No. 1 (2021): Jurnal Ilmu dan Teknologi Kelautan Tropis; 97-112
2620-309X
2087-9423
10.29244/jitkt.v13i1
 
Language eng
 
Relation https://journal.ipb.ac.id/index.php/jurnalikt/article/view/34765/21594
 
Rights Copyright (c) 2021 Jurnal Ilmu dan Teknologi Kelautan Tropis
https://creativecommons.org/licenses/by-sa/4.0
 

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