[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127313-en":3,"doc-seo-127313-105":30,"detail-sidebar-cat-0-en-105":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},127313,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",6,"Technology","Implementation of Machine Learning Model to Detect Sign Language Movement in SIBI Learning Media","Research develops a web-based Indonesian Sign Language System (SIBI) learning application that integrates motion detection to raise the accuracy of sign practice. It addresses the absence of gesture validation tools on existing platforms, despite SIBI being an official system in Indonesian special needs education. Using the Design Sprint workflow and Microsoft Azure Machine Learning, the model recognizes SIBI hand gestures and provides real-time feedback. Experiments show 99.82% accuracy on public data and 96.4% on private data, with 86% user satisfaction in beta testing.","Implementation of Machine Learning Model to Detect Sign Language Movement in SIBI Learning Media  \nLeni Fitriani1*, Dede Kurniadi2, Ilham Syahidatul Rajab3  \n1,2,3Department of Computer Science, Institut Teknologi Garut, Garut, West Java, Indonesia  \nE-mail:1*[leni.fitriani@itg.ac.id](leni.fitriani@itg.ac.id), [2](2dede.kurniadi@itg.ac.id)[dede.kurniadi@itg.ac.id](2dede.kurniadi@itg.ac.id), [3](3ilhamsyahidatulr@gmail.com)[ilhamsyahidatulr@gmail.com](3ilhamsyahidatulr@gmail.com)  \n(Received: 27 Nov 2024, revised: 10 Dec 2024, accepted: 11 Dec 2024)  \nAbstract  \nThis research focuses on the development of a web-based Indonesian Sign Language System (SIBI) learning application with motion detection to improve the precision of sign language practice. Despite the government's introduction of SIBI as an official system, existing platforms lack tools to validate the accuracy of hand movements. Using the Design Sprint methodology—comprising Understand, Define, Sketch, Decide, Prototype, and Validate phases—this study employs Microsoft Azure Machine Learning to create a motion detection model capable of recognizing SIBI gestures. The application offers an interactive learning experience, allowing users to practice and receive real-time feedback on their accuracy. Initial trials demonstrated high prediction accuracy, achieving 99.82% on public datasets and 96.4% on private datasets. Beta testing revealed an 86% satisfaction rate among users, indicating the application’s effectiveness in enhancing the learning process. By providing accessibility through standard web browsers and incorporating advanced motion detection, this application contributes to inclusivity, facilitating broader public understanding and interest in learning sign language.  \nKeywords: Azure, Design Sprint, Learning Media, Machine Learning, SIBI  \nI. INTRODUCTION  \nSign language is a visual language of communication used by the deaf and mute through hand gestures, facial expressions, and body movements to represent letters, numbers, and words [1] . In Indonesia, there are two sign languages: Indonesian Sign Language (BISINDO) and Indonesian Sign Language System (SIBI) [2] . SIBI is based on the American Sign Language (ASL) and is officially used in Special Needs Schools (SLB) under the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) . Nasir's research (2021) showed that students adapt differently to SIBI than their peers [3]. However, sign language communication is hindered by limitations in sign language proficiency [4] . While literature teaches sign language, the focus is on its usage [1] . The government has created the [pmpk.kemdikbud.go.id](pmpk.kemdikbud.go.id)[ ](pmpk.kemdikbud.go.id)website for SIBI learning through videos, but it needs motion detection. Hence, a machine learning application is required to detect Indonesian sign language hand gestures.  \nThe first referenced study is based on the arrangement of smartphone photos over time, causing users to forget to search for similar photos when capturing an image. This study resulted in an application that categorizes photos using a model with an accuracy of 96%, precision of 93%, and success rate of 93%[5], [6] . The second referenced study is based on deaf  \nstudents who understand words through images and require supplementary aids. This study produced a video-based educational application to assist the learning process [7] . The third study is motivated by non-verbal communication challenges among some individuals. Sign language is used to communicate with disabled individuals. This study created a prototype application using Microsoft Kinect to detect hand movements with an accuracy ranging from 75% to 87.5%[8] . The fourth study discusses the challenges disabled individuals face in communicating and interacting with others due to varying sign language comprehension. It resulted in an application that detects movements and provides text and audio outputs. The model achieve","cbCaiv9kSWa3vvFg","https://ap.wps.com/l/cbCaiv9kSWa3vvFg","pdf",989222,1,9,"English","en",105,"# Introduction\n## Related Work and Motivation\n## Research Approach and Problem Statement\n# Abstract\n## Study Goals and Outcomes","[{\"question\":\"What problem does the study address in SIBI learning platforms?\",\"answer\":\"Existing SIBI platforms lack tools to validate whether hand movements match the intended gestures, limiting practice accuracy.\"},{\"question\":\"How does the research build the motion detection model?\",\"answer\":\"It follows the Design Sprint phases and uses Microsoft Azure Machine Learning to train a model that recognizes SIBI gestures.\"},{\"question\":\"What performance and user results were reported?\",\"answer\":\"Initial trials reached 99.82% on public datasets and 96.4% on private datasets, while beta testing showed an 86% satisfaction rate among users.\"}]","Implementation of Machine Learning Model to Detect Sign Language Movement in SIBI Learning Media | PDF",1785938239,23,{"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},"implementation-of-machine-learning-model-to-detect-sign-language-movement-in-sibi-learning-media","",{"@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/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/implementation-of-machine-learning-model-to-detect-sign-language-movement-in-sibi-learning-media/127313/",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","2026-08-24","2026-08-05",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},"What problem does the study address in SIBI learning platforms?","Question",{"text":76,"@type":77},"Existing SIBI platforms lack tools to validate whether hand movements match the intended gestures, limiting practice accuracy.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the research build the motion detection model?",{"text":81,"@type":77},"It follows the Design Sprint phases and uses Microsoft Azure Machine Learning to train a model that recognizes SIBI gestures.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance and user results were reported?",{"text":85,"@type":77},"Initial trials reached 99.82% on public datasets and 96.4% on private datasets, while beta testing showed an 86% satisfaction rate among users.","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},[94,98,102,106,111,114,119,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]