[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120822-id":3,"doc-seo-120822-113":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120822,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",54,"Penelitian & Laporan","Analisis Kerentanan Banjir Menggunakan Data Citra Satelit dan Machine Learning di Kota Surabaya","Analisis ini menyusun peta kerentanan banjir di Kota Surabaya dengan memanfaatkan metode Frequency Ratio (FR) serta data citra satelit Sentinel-1. Penafsiran citra diintegrasikan menggunakan kemampuan Machine Learning yang dipadukan dengan Sistem Informasi Geografis (SIG) agar proses klasifikasi lebih cepat. Penelitian membandingkan tiga pendekatan Machine Learning, yaitu Bayes, Random Forest (RF), dan Support Vector Machine (SVM), untuk memperoleh indeks kerentanan banjir. Hasil menunjukkan sekitar 61,23% area tergolong aman, sedangkan sisanya berada pada kelas kerentanan rendah, sedang, dan tinggi.","Jurnal Aplikasi Teknik Sipil Volume 21, Nomor 3, Agustus 2023  \nJournal homepage: [http://iptek.its.ac.id/index.php/jats](http://iptek.its.ac.id/index.php/jats)  \n| Analisis Kerentanan Banjir Menggunakan Data Citra Satelit dan Machine Learning di Kota Surabaya\u003Cbr>Ahmad Saifudin1,*, Mahendra Andiek Maulana1, Anak Agung Ngurah Satria Damarnegara1\u003Cbr>Departemen Teknik Sipil, Institut Teknologi Sepuluh Nopember, Surabaya1\u003Cbr>Koresponden*, Email: [saisupen@live.com](saisupen@live.com) |  |  |\n| --- | --- | --- |\n| Info Artikel |  | Abstract |\n| Diajukan\u003Cbr>Diperbaiki\u003Cbr>Disetujui | 31 Januari 2023\u003Cbr>25 Mei 2023\u003Cbr>27 Juli 2023 | Flood is a natural disaster typically happen after rain Floods have an impact on damage, soan efficient flood susceptibility ssessment is needed. Satellite imagery can be used to help detect flooding on a broad scale. One of th challenges in processing image data is imag interpretation. By utilizing Machine Learning capabilities that ar integrated with Geographic nformation Systems, image interpretation can be c rried out quickly. However, the challenge of using satellite imagery is the lack of large-cale flood datasets. In this paper, we presen hree Machine Learning approaches, namely Bayes, R in Forest (RF), and Support Vector Machine (SVM) which are then analyzed using the Freq ency Ratio method to obtain a flood susceptibility index. By utilizing the available Sentinel-1 imagery, the analysis in this study |\n| Keywords: flood susceptibility, machine learning, GIS, sentinel-1 |  | shows that 61.23 percent of the total area is classified as safe from flood susceptibility and therest is classified as low, medium, and high flood susceptibility. |\n| Kata kunci: peta rawan banjir; machine learning; SIG; sentinel-1 |  | Abstrak\u003Cbr>Banjir merupakan bencana alam yang biasanya terjadi saat hujan. Banjir berdampakpadakerusakan sehingga diperlukannya penilaian kerentanan banjir yang efisien. Citra satelit dapat digunakan untuk membantu mendeteksi banjir dalam skala yang luas. Salah satu tantangan dalam mengolah data citra adalah interpretasi citra. Dengan memanfaatkankemampuan Machine Learning yang diintegrasikan dengan Sistem Informasi Geografis, interpretasi citra dapat dilakukan dengan cepat. Namun, tantangan dari penggunaan citrasatelit adalah kurangnya dataset kejadian banjir dalam skala besar. Pada paper ini, kami menyajikan tiga pendekatan Machine Learning, yaituBayes, Rain Forest (RF), dan Support Vector Machine (SVM) yang kemudian dianalisis menggunakan metode Frequency Ratiosehingga didapatkan indeks kerentanan banjir. Dengan memanfaatkan citra Sentinel-1 yang tersedia, analisis dalam penelitian ini menunjukkan bahwa sebesar 61,23 persen dari total luas wilayah tergolong aman dari kerentanan banjir sedangkan sisanya termasuk dalam kerentanan banjir rendah, sedang, dan tinggi. |\n\n1. Pendahuluan  \nKota Surabaya terletak di dataran rendah yang sebagian besar wilayahnya memiliki tinggi permukaan tanah sekitar 3- 6 meter di ataspermukaan air laut. Hal tersebut menyebabkan Kota Surabaya rentan terjadi banjir. Berdasarkan kondisi tersebut maka diperlukan penelitian terkait dengan analisis wilayah rentan banjir. Informasi mengenai karakteristik banjir beserta pengaruhnya sangat penting bagi otoritas penanggulangan bencana banjir maupun kebijakan pemerintahuntuk pencegahan maupun mitigasi banjir [1] . Teknologi penginderaan jauh yang dikombinasikan dengan Sistem Informasi Geografis dapat dimanfaatkan untuk menganalisa daerah rawan banjir, khususnya di Kota Surabaya.  \nPemetaan banjir merupakan proses yang menjelaskan meluasnya genangan air menuju area yang kering akibat dari hujan atau meningkatnya muka air sungai [2] . Pemetaan banjir dapat digunakan sebagai media komunikasi risiko banjirdengan informasi mengenai tren histori banjir, prediksi banjir  \nyang akan datang dan identifikasi lokasi yang rentan terkenabanjir [3] . Pemetaan banjir dapat memberikan informasi mengenai daerah mana saja yang perlu perhatian ","cbCaimJ0YOk237k0","https://ap.wps.com/l/cbCaimJ0YOk237k0","pdf",384318,1,8,"Indonesian","id",113,"# Pendahuluan\n# Metode\n## Area penelitian\n## Data yang digunakan","[{\"question\":\"Apa tujuan utama penelitian dalam dokumen ini?\",\"answer\":\"Membuat peta kerentanan banjir di Surabaya menggunakan metode Frequency Ratio dengan memanfaatkan citra satelit Sentinel-1.\"},{\"question\":\"Pendekatan Machine Learning apa saja yang digunakan untuk analisis kerentanan banjir?\",\"answer\":\"Tiga pendekatan yang digunakan adalah Bayes, Random Forest (RF), dan Support Vector Machine (SVM).\"},{\"question\":\"Bagaimana hasil klasifikasi kerentanan banjir pada wilayah studi?\",\"answer\":\"Sekitar 61,23% dari total luas wilayah tergolong aman dari kerentanan banjir, sedangkan sisanya termasuk kelas rendah, sedang, dan tinggi.\"}]","Analisis Kerentanan Banjir Menggunakan Data Citra Satelit dan Machine Learning di Kota Surabaya | PDF",1785732189,12,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"flood-vulnerability-analysis-using-satellite-imagery-and-machine-learning-in-surabaya-city","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/id/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/id/document/flood-vulnerability-analysis-using-satellite-imagery-and-machine-learning-in-surabaya-city/120822/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-13","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},"Apa tujuan utama penelitian dalam dokumen ini?","Question",{"text":76,"@type":77},"Membuat peta kerentanan banjir di Surabaya menggunakan metode Frequency Ratio dengan memanfaatkan citra satelit Sentinel-1.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Pendekatan Machine Learning apa saja yang digunakan untuk analisis kerentanan banjir?",{"text":81,"@type":77},"Tiga pendekatan yang digunakan adalah Bayes, Random Forest (RF), dan Support Vector Machine (SVM).",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana hasil klasifikasi kerentanan banjir pada wilayah studi?",{"text":85,"@type":77},"Sekitar 61,23% dari total luas wilayah tergolong aman dari kerentanan banjir, sedangkan sisanya termasuk kelas rendah, sedang, dan tinggi.","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":24},{"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":46,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]