[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121603-id":3,"doc-seo-121603-113":31,"detail-sidebar-cat-0-id-113":84},{"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},121603,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",52,"Teknologi","Machine Learning System untuk Mendeteksi Gerakan Tubuh Menggunakan Library MediaPipe - Jurnal Ilmiah Teknik Informatika","Komunikasi dengan penyandang disabilitas pendengaran dan bicara sering menjadi tantangan karena tidak semua pihak mampu memahami bahasa isyarat. Sistem ini bertujuan membangun model machine learning untuk mengenali gerakan tangan dalam ejaan fingerspelling menggunakan American Sign Language (ASL) secara real-time melalui kamera. Model memanfaatkan data citra dan teknik computer vision untuk melatih deep learning agar melakukan proses, klasifikasi, dan prediksi sinyal secara akurat dengan dukungan jaringan saraf dalam berlapis.","FORMAT: Jurnal Ilmiah Teknik Informatika  \nAkreditasi Sinta 5 RISTEKBRIN No. 164/E/KPT/2021; E-ISSN 2722-7162 | P-ISSN 2089-5615  \nVol. 14, No. 1, January 2025, 81-89. 2025  \nMachine Learning System untuk Mendeteksi Gerakan  \nTubuh Menggunakan Library Mediapipe  \nIrfan Nurdiansyah 1 ; Reni Utami 2 ; Muchamad Sandy3  \n--------------  \n Fakultas Ilmu Komputer, Universitas Dian Nusantara, Jl. Tj, Duren Barat. 2 No.1, RT.1/RW.5   \n[1](1 irfan.nurdiansyah@dosen.undira.ac.id)[ ](1 irfan.nurdiansyah@dosen.undira.ac.id)[irfan.nurdiansyah@dosen.undira.ac.id](1 irfan.nurdiansyah@dosen.undira.ac.id) ,, [2](2 reni.utami@dosen.undira.ac.id)[ ](2 reni.utami@dosen.undira.ac.id)[reni.utami@dosen.undira.ac.id](2 reni.utami@dosen.undira.ac.id) , [3](3 muchamad.sandy@dosen.undira.ac.id)[ ](3 muchamad.sandy@dosen.undira.ac.id)[muchamad.sandy@dosen.undira.ac.id](3 muchamad.sandy@dosen.undira.ac.id)  \n-  \nKata kunci:  \nMachine learning, Bahasa Isyarat, Mediapipe, ASL  \nAbstract  \nCommunication with people with hearing and speech disabilities is often challenging. Sign language is the primary tool that helps them convey thoughts and feelings, but it is often difficult for those who are not used to it to understand. This project aims to develop a machine learning model to recognize hand gestures in spelling fingers using American Sign Language (ASL) . The model uses image data and Computer Vision techniques to train a deep learning algorithm that can recognize signals in real-time through a camera. The system utilizes deep neural networks that work through layers of nodes to process, classify, and predict cues accurately  \nPendahuluan  \nBahasa isyarat, sebagai modalitas komunikasi utama bagi komunitas tunarungu, memiliki peran krusial dalam interaksi sosial dan aksesibilitas informasi. Namun, perbedaan antara bahasa isyarat dan bahasa lisan/tulis seringkali menjadi penghalang komunikasi [1] . Oleh karena itu, pengembangan sistem machine learning untuk deteksi bahasa isyarat menjadi penting gunamenjembatani kesenjangan komunikasi ini. Sistem ini diharapkan mampu menerjemahkan bahasa isyarat ke dalam bahasa yang dapat dipahami oleh masyarakat umum, sehingga meningkatkan inklusi dan partisipasi komunitas tunarungu dalam berbagai aspek kehidupan.  \nPenelitian ini difokuskan pada perancangan dan implementasi sistem machine learning [2] yang mampu mengenali bahasa isyarat. Sistem ini memanfaatkan pemrosesan citra [3] dan deep learning [4] untuk mengidentifikasi gerakan tangan [5], ekspresiwajah, dan elemen visual lainnya yang merupakan ciri khas bahasa isyarat. Tujuan utama adalah menciptakan sistem yang akurat dan efisien dalam menerjemahkan bahasa isyarat ke dalam teks atau suara [6], sehingga memudahkan komunikasi antara individu tunarungu dan masyarakat umum.  \nDalam penelitian ini, kami memanfaatkan MediaPipe [7], sebuah framework sumber terbuka yang dikembangkan oleh Google, untuk memfasilitasi deteksi dan pelacakan gerakan tangan serta ekstraksi fitur-fitur penting dari bahasa isyarat. MediaPipe menyediakan solusi yang efisien dan real-time untuk pemrosesan data multimodal, termasuk video. Modul Hand Pose Estimation [8] dari MediaPipe memungkinkan identifikasi titik-titik kunci (landmark) pada tangan dengan akurasi yang cukup tinggi. Informasi ini kemudian digunakan sebagai input untuk model machine learning yang bertugas menerjemahkan bahasa isyarat.  \nPenggunaan MediaPipe [9] dalam penelitian ini memberikan beberapa keuntungan. Pertama, framework ini mudahdiintegrasikan dengan berbagai bahasa pemrograman dan platform, sehingga mempercepat proses pengembangan sistem. Kedua, MediaPipe telah dioptimalkan untuk kinerja tinggi, memungkinkan pemrosesan video secara real-time tanpa mengorbankanakurasi. Ketiga, ketersediaan model pre-trained untuk deteksi tangan [10] dan estimasi pose mengurangi kebutuhan sumber dayakomputasi dan waktu pelatihan model. Dengan memanfaatkan MediaPipe, penelitian ini dapat lebih fokus pada pengembanganalgoritma machine learn","cbCaidl0PwHadUR2","https://ap.wps.com/l/cbCaidl0PwHadUR2","pdf",661407,3,1,9,"Indonesian","id",113,"# Pendahuluan\n## Peran bahasa isyarat dan kebutuhan penerjemahan\n## Fokus penelitian dan tujuan sistem\n## Pemanfaatan MediaPipe (Hand Pose Estimation)\n## Keunggulan penggunaan MediaPipe\n## Batasan penelitian dan rencana pengembangan\n# Metode Penelitian\n## Perangkat perekam dan komputer\n## Tahapan proses (flowchart)\n## Akuisisi gambar dari kamera\n## Deteksi dan pelacakan tangan","[{\"question\":\"Mengapa MediaPipe digunakan dalam sistem?\",\"answer\":\"MediaPipe membantu deteksi dan pelacakan gerakan tangan serta ekstraksi fitur landmark tangan secara efisien, real-time, dan lebih cepat karena tersedia model pre-trained.\"}]","Machine Learning System untuk Mendeteksi Gerakan Tubuh Menggunakan Library MediaPipe - Jurnal Ilmiah Teknik Informatika | PDF",1785736439,14,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":29},"machine-learning-system-for-detecting-body-movement-using-mediapipe-library-journal-of-computer-science","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/teknologi/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/machine-learning-system-for-detecting-body-movement-using-mediapipe-library-journal-of-computer-science/121603/",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","2026-08-15","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa MediaPipe digunakan dalam sistem?","Question",{"text":76,"@type":77},"MediaPipe membantu deteksi dan pelacakan gerakan tangan serta ekstraksi fitur landmark tangan secara efisien, real-time, dan lebih cepat karena tersedia model pre-trained.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,91,95,99,103,107,111,115,117,121,125],{"id":87,"doc_module":4,"doc_module_name":47,"category_name":88,"show_sort_weight":89,"slug":90},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":92,"doc_module":4,"doc_module_name":47,"category_name":93,"show_sort_weight":89,"slug":94},48,"Cerita & Novel","story-novel",{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":89,"slug":98},56,"Gaya Hidup","lifestyle",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":89,"slug":102},51,"Komik","comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":89,"slug":106},53,"Layanan Kesehatan","healthcare",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":89,"slug":110},54,"Penelitian & Laporan","research-report",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":89,"slug":114},49,"Sastra","literature",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":89,"slug":116},"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":89,"slug":120},50,"Ujian","exam",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":89,"slug":124},57,"Umum","general",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":4,"slug":128},181,"Formulir","formulir"]