[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120270-id":3,"doc-seo-120270-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},120270,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",54,"Penelitian & Laporan","Pemetaan Lamun Menggunakan Algoritme Machine Learning dengan Citra Sentinel-2A di Perairan Pulau Sebesi - Seagrass Mapping Using Machine Learning Algorithms with Sentinel-2A Imagery in Sebesi Island","Pulau Sebesi memiliki ekosistem pesisir yang relatif subur dan produktif, termasuk lamun yang berperan penting sebagai pelindung pantai, feeding ground bagi ikan, serta penghalang dari kerusakan akibat sedimentasi. Kerusakan ekosistem lamun berdampak pada ekosistem laut lain karena banyak siklus hidup biota bergantung pada keberadaan lamun. Penelitian ini mengkaji sebaran lamun melalui pemanfaatan penginderaan jauh dengan machine learning menggunakan metode Maximum Likelihood (MLH) dan Random Forest (RF) berbasis citra Sentinel-2A. Hasil pengolahan citra menunjukkan akurasi RF lebih tinggi (83,3%) dibanding MLH (66,6%) berdasarkan uji overall accuracy.","PEMETAAN LAMUN MENGGUNAKAN ALGORITME MACHINE LEARNING DENGANCITRA SENTINEL-2A DI PERAIRAN PULAU SEBESI SEAGRASS MAPPING USING MACHINE LEARNING ALGORITHMS WITH SENTINEL-2A IMAGERY IN SEBESI ISLAND  \nWilldan Aprizal Arifin*, La Ode Alam Minsaris, Luthfi Anzani, Tirta Samudera Ramadhani, Marcella Grace Angelique Lubis, Afrizal Dzikrillah, Yulda, Oki Suprianto  \nProgram Studi Sistem Informasi Kelautan, Universitas Pendidikan Indonesia Jl. Dr. Setiabudi No.229, Isola, Kec. Sukasari, Kota Bandung, Jawa Barat 40154  \n*Coressponding [author email: ](author email: willdanarifin@upi.edu)[willdanarifin@upi.edu](author email: willdanarifin@upi.edu)  \nSubmitted: 28 February 2024 /Revised: 18 April 2025 / Accepted: 21 April 2025  \n[http://doi.org/10.21107/jk.v18i1.25025](http://doi.org/10.21107/jk.v18i1.25025)  \nABSTRAK  \nPulau Sebesi memiliki potensi yang menjanjikan baik di daratan maupun pesisirnya. Wilayah pesisir Pulau Sebesi memiliki ekosistem lengkap yang membuat wilayah tersebut relatif subur dan produktif, seperti ekosistem mangrove, terumbu karang, dan lamun. Lamun merupakan tumbuhan berbunga (angiospermae) yang terdiri atas akar, daun, bunga, rimpang, dan buah yang memiliki peran sangat penting. Jika ekosistem lamun mengalami kerusakan, maka akan berdampak pada ekosistem lainnyadi laut karena banyak hewan laut yang siklus hidupnya bergantung pada keberadaan lamun. Pulau Sebesi belum memiliki informasi terbaru terkait luasan lamun beserta klasifikasinya yang dapat dilihat secara visual di pulau tersebut. Tujuan penelitian ini adalah untuk mengkaji sebaran lamun menggunakan machine learning memakai metode MLH dan RF kemudian dibandingakn untukmendapat model terbaik Metode yang digunakan dalam pemantauan yang berkelanjutan dalam mencegah kerusakan ekosistem lamun, yaitu dengan memanfaatkan teknologi penginderaan jauh. Pemantauan tersebut menggunakan machine learning yang memanfaatkan metode Maximum Likelihood (MLH) dan Random Forest (RF) dengan citra sentinel-2A. Hasil olah citra menunjukkanakurasi lebih baik pada metode RF, yaitu sebesar 83,3% dan MLH sebesar 66. 6%. Hal tersebut telah diuji akurasinya menggunakan perhitungan overall accuracy. Berdasarkan hal tersebut, makaklasifikasi RF lebih baik dibandingkan dengan klasifikasi MLH.  \nKata kunci: lamun, machine learning, sentinel-2A  \nABSTRACT  \nSebesi Island has promising potential both on land and in its coastal areas. The coastal area of Sebesi Island has a complete ecosystem that makes the area relatively fertile and productive, such as mangrove ecosystems, coral reefs, and seagrass. Seagrass is a flowering plant (angiosperm) that consists of roots, leaves, flowers, rhizomes, and fruits, and plays a very important role. If the seagrass ecosystem is damaged, it will impact other marine ecosystems because many marine animals have life cycles that depend on the presence of seagrass. Sebesi Island does not yet have the latest information regarding the area and classification of seagrass that can be visually seen on the island. The purpose of this research is to examine the distribution of seagrass using machine learning with the MLH and RF methods, which are then compared to obtain the best model. The method used for sustainable monitoring to prevent damage to the seagrass ecosystem is by utilizing remote sensing technology. This monitoring uses machine learning that applies the Maximum Likelihood (MLH) and Random Forest (RF) methods with Sentinel-2A imagery. The image processing results show better accuracy in the RF method, which is 83.3%, and MLH is 66. 6%. These accuracies were tested using overall accuracy calculations. Based on this, the RF classification is better than the MLH classification.  \nKeywords: seagrass, machine learning, sentinel-2A   \nPENDAHULUAN  \nPulau Sebesi merupakan pulau yang terletak di Kabupaten Lampung Selatan dan terdiri atas satu desa, yaitu Desa Tejang (Kurniasih dan Tejapermana, 2018) . Pulau Sebesi memilikipotensi yang menjanjikan baik di daratan ","cbCaitqzs18J2uaC","https://ap.wps.com/l/cbCaitqzs18J2uaC","pdf",718665,2,1,8,"Indonesian","id",113,"# Pendahuluan\n## Potensi dan urgensi ekosistem lamun di Pulau Sebesi\n## Konsep penginderaan jauh untuk pemantauan berkelanjutan\n## Metode machine learning (MLH dan RF)","[{\"question\":\"Mengapa pemantauan sebaran lamun di Pulau Sebesi penting?\",\"answer\":\"Lamun berperan menjaga pantai, menyediakan habitat/feeding ground, dan membantu mengurangi dampak sedimentasi pada ekosistem terumbu karang. Kerusakan lamun juga memengaruhi ekosistem laut lain karena banyak biota bergantung pada lamun.\"},{\"question\":\"Metode machine learning apa yang digunakan untuk memetakan lamun?\",\"answer\":\"Penelitian menggunakan dua metode, yaitu Maximum Likelihood (MLH) dan Random Forest (RF), dengan memanfaatkan citra Sentinel-2A.\"},{\"question\":\"Metode mana yang menghasilkan akurasi lebih baik dan berapa nilainya?\",\"answer\":\"Random Forest (RF) menghasilkan akurasi lebih baik, yaitu 83,3%, sedangkan Maximum Likelihood (MLH) sebesar 66,6%. Perbandingan dilakukan melalui perhitungan overall accuracy.\"}]","Pemetaan Lamun Menggunakan Algoritme Machine Learning dengan Citra Sentinel-2A di Perairan Pulau Sebesi - Seagrass Mapping Using Machine Learning Algorithms with Sentinel-2A Imagery in Sebesi Island | PDF",1785729180,12,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"seagrass-mapping-using-machine-learning-algorithms-with-sentinel-2a-imagery-in-sebesi-island-pemetaan-lamun-menggunakan-algoritme-machine-learning-dengan-citra-sentinel-2a-di-perairan-pulau-sebesi","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/id/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/seagrass-mapping-using-machine-learning-algorithms-with-sentinel-2a-imagery-in-sebesi-island-pemetaan-lamun-menggunakan-algoritme-machine-learning-dengan-citra-sentinel-2a-di-perairan-pulau-sebesi/120270/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-10","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa pemantauan sebaran lamun di Pulau Sebesi penting?","Question",{"text":76,"@type":77},"Lamun berperan menjaga pantai, menyediakan habitat/feeding ground, dan membantu mengurangi dampak sedimentasi pada ekosistem terumbu karang. Kerusakan lamun juga memengaruhi ekosistem laut lain karena banyak biota bergantung pada lamun.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode machine learning apa yang digunakan untuk memetakan lamun?",{"text":81,"@type":77},"Penelitian menggunakan dua metode, yaitu Maximum Likelihood (MLH) dan Random Forest (RF), dengan memanfaatkan citra Sentinel-2A.",{"name":83,"@type":74,"acceptedAnswer":84},"Metode mana yang menghasilkan akurasi lebih baik dan berapa nilainya?",{"text":85,"@type":77},"Random Forest (RF) menghasilkan akurasi lebih baik, yaitu 83,3%, sedangkan Maximum Likelihood (MLH) sebesar 66,6%. Perbandingan dilakukan melalui perhitungan overall accuracy.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]