[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-118951-113":53,"doc-detail-118951-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","benthic-habitat-identification-model-based-on-machine-learning-yolov5","Model Identifikasi Habitat Bentik Berbasis Machine Learning - YOLOv5","","Penelitian ini mengembangkan model identifikasi habitat bentik berbasis machine learning menggunakan algoritma YOLOv5 untuk mengurangi beban kerja identifikasi manual yang memakan waktu dan sumber daya besar. Proses meliputi pengumpulan, identifikasi, dan pelabelan dataset citra habitat bentik, kemudian pelatihan model. Evaluasi dilakukan memakai confusion matrix serta uji validasi pada 120 foto dari berbagai kelas habitat. Model memperoleh accuracy 46,6%, recall 54,4%, precision 76,6%, dan F1-score 63,6%, sehingga belum layak untuk aplikasi dunia nyata, namun berpotensi untuk otomasi dan analisis citra bawah air, dengan peluang perbaikan melalui preprocessing dan penambahan dataset.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/benthic-habitat-identification-model-based-on-machine-learning-yolov5/118951/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/benthic-habitat-identification-model-based-on-machine-learning-yolov5/118951.png","ImageObject",300,407,{"name":89,"@type":90},"Emma Wilson","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-03",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tujuan penelitian ini mengembangkan model identifikasi habitat bentik?","Question",{"text":110,"@type":111},"Mengembangkan model identifikasi habitat bentik berbasis machine learning dengan YOLOv5 untuk mengatasi proses identifikasi manual yang memakan waktu dan sumber daya besar.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Tahapan apa saja yang dilakukan dalam penelitian ini?",{"text":115,"@type":111},"Penelitian meliputi pengumpulan dataset gambar habitat bentik, identifikasi dan pelabelan objek, lalu pelatihan model menggunakan algoritma YOLOv5.",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana hasil evaluasi model dan apa kesimpulannya?",{"text":119,"@type":111},"Model dievaluasi dengan confusion matrix dan uji validasi 120 foto. Hasil menunjukkan accuracy 46,6%, recall 54,4%, precision 76,6%, dan F1-score 63,6%, sehingga belum layak untuk aplikasi dunia nyata.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},118951,1785721162,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":74,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":127,"read_time":77},962084928432,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","ABSTRAK  \nPenelitian ini bertujuan untuk mengembangkan model identifikasi habitat bentik berbasis machine learning dengan menggunakan algoritma YOLOv5 . Habitat bentik memainkan peran penting dalam ekosistem laut maupunkebutuhan manusia. Identifikasi dan analisis habitat bentik secara manual merupakan tugas yang memakan waktu dan sumber daya besar. Oleh karenaitu, penelitian ini berupaya mencari solusi dengan memanfaatkan teknologi machine learning untuk mengatasi masalah tersebut. Penelitian ini melibatkan beberapa tahap, termasuk pengumpulan dataset gambar habitat bentik, identifikasi dan pelabelan objek dalam dataset, serta proses pelatihan model menggunakan algoritma YOLOv5 . Hasil dari training model dievaluasi menggunakan confusion matrix model dan uji validasi dengan 120 foto dari berbagai kelas habitat bentik. Hasil penelitian menunjukkan bahwa model memiliki nilai accuracy 46,6% . Nilai recall sebesar 54,4%, nilai precision sebesar 76,6%, dan nilai F1-score sebesar 63,6% yang mengindikasikan bahwa hasil dari identifikasi objek habitat bentik dapat dikatakan tidak layak untuk aplikasidunia nyata. Model ini memiliki potensi untuk mengotomatisasi tugasidentifikasi dan memudahkan analisis citra bawah air. Meskipun masih ada ruang untuk perbaikan secara berkala dari segi algoritma, tambahan tahap preprocessing, dan penambahan jumlah dataset. Pengembangan model ini membukapotensi untuk aplikasi lebih lanjut dalam pemantauan dan konservasi ekosistem laut.  \nKata kunci : Machine Learning; YOLOv5; Deteksi Objek; Habitat Bentik; Identifikasi Otomatis  \nABSTRACT  \nThe research aimed to develop a benthic habitat-based identification machine learning model using the YOLOv5 algorithm. Benthic habitats plays an important role in marine ecosystems and human needs. Manual identification and analysis of benthic habitats were time-consuming and resource-intensive. Therefore, the research sought to find a solution by utilizing machine learning technology to resolve the issue. The research involved several stages, including collecting benthic habitat images, identifying and labelling objects in the dataset, and training the model using the YOLOv5 algorithm. The results of the model training were evaluated using a confusion matrix model and validation tests with 120 photos from various benthic habitat classes. The results showed that the model had an accuracy value of 46,6%, a recall value of 54,4%, a precision value of 76,6%, and an F1-score value of 63,6%, which indicated that the model could be considered unfit for real-world applications. This model had the potential to automate identification tasks and facilitate underwater image analysis. Although there was still room for improvement in terms of algorithm, additional preprocessing stages, and increasing the amount of data gradually. The development of this model opened up the potential for further applications in monitoring and conserving marine ecosystems.  \nKey words: Machine Learning; YOLOv5; Object Detection; Benthic Habitat; Automated Identification","cbCaidYUTpxQYYG2","https://ap.wps.com/l/cbCaidYUTpxQYYG2","pdf",135401,"Indonesian","# Abstrak\n## Tujuan Penelitian\n## Metode (YOLOv5 dan Tahapan Pengembangan Model)\n## Evaluasi dan Hasil\n## Implikasi dan Rekomendasi Pengembangan Lanjutan","[{\"question\":\"Apa tujuan penelitian ini mengembangkan model identifikasi habitat bentik?\",\"answer\":\"Mengembangkan model identifikasi habitat bentik berbasis machine learning dengan YOLOv5 untuk mengatasi proses identifikasi manual yang memakan waktu dan sumber daya besar.\"},{\"question\":\"Tahapan apa saja yang dilakukan dalam penelitian ini?\",\"answer\":\"Penelitian meliputi pengumpulan dataset gambar habitat bentik, identifikasi dan pelabelan objek, lalu pelatihan model menggunakan algoritma YOLOv5.\"},{\"question\":\"Bagaimana hasil evaluasi model dan apa kesimpulannya?\",\"answer\":\"Model dievaluasi dengan confusion matrix dan uji validasi 120 foto. Hasil menunjukkan accuracy 46,6%, recall 54,4%, precision 76,6%, dan F1-score 63,6%, sehingga belum layak untuk aplikasi dunia nyata.\"}]","Model Identifikasi Habitat Bentik Berbasis Machine Learning - YOLOv5 | PDF"]