[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119770-id":3,"doc-seo-119770-113":31,"detail-sidebar-cat-0-id-113":93},{"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},119770,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","Analisis Perbandingan Algoritma Machine Learning Dan Deep Learning Untuk Klasifikasi Citra SIBI","Penelitian ini membahas klasifikasi citra Sistem Isyarat Bahasa Indonesia (SIBI) sebagai solusi untuk mempermudah komunikasi bagi penyandang tuli atau bisu yang tidak dapat berkomunikasi verbal. Bahasa isyarat dipandang sebagai masalah klasifikasi kompleks dengan sintaks dan tata bahasa yang berbeda, sehingga diperlukan metode computer vision yang mampu mengenali citra secara otomatis. Analisis dilakukan dengan membandingkan waktu komputasi dan performa klasifikasi antara K-Nearest Neighbors (KNN), Support Vector Machine (SVM), serta CNN berbasis transfer learning. Arsitektur transfer learning yang digunakan meliputi Xception, ResNet50, VGG15, dan MobileNetV2. Hasil 5 cross-validation menunjukkan CNN dengan Xception mencapai F1 Score tertinggi 99,57% dan waktu training rata-rata 1.387 detik, sedangkan KNN (K=1) paling cepat dengan waktu training 0,03 detik namun F1 Score 86,95%.","ANALISIS PERBANDINGAN ALGORITMA MACHINE LEARNING DAN DEEP LEARNING UNTUK KLASIFIKASI CITRA SISTEM ISYARAT BAHASA  \nINDONESIA (SIBI)  \nMohammad Farid Naufal*1, Selvia Ferdiana Kusuma2  \n1Universitas Surabaya, Surabaya, 2Politeknik Elektronika Negeri Surabaya [Email:](Email:1 faridnaufal@staff.ubaya.ac.id)[1](Email:1 faridnaufal@staff.ubaya.ac.id)[ faridnaufal@staff.ubaya.ac.id](Email:1 faridnaufal@staff.ubaya.ac.id), [2](2selvia@pens.ac.id)[selvia@pens.ac.id](2selvia@pens.ac.id)  \n*Penulis Korespondensi  \n(Naskah masuk: 19 Desember 2022, diterima untuk diterbitkan: 26 Juli 2023)  \nAbstrak  \nTerdapat orang yang tidak mampu berkomunikasi secara verbal yang menyebabkan kesulitan dalamberkomunikasi. Orang tersebut mengalami gangguan seperti tuli atau bisu. Mereka hanya dapat berkomunikasimelalui bahasa isyarat salah satunya adalah Sistem Isyarat Bahasa Indonesia (SIBI) . Pengenalan Bahasa Isyarat adalah permasalahan klasifikasi yang kompleks untuk dipecahkan. Setiap bahasa isyarat memiliki sintaks dan tata bahasanya sendiri. Computer vision adalah sebuah Teknik yang digunakan komputer untuk melakukan klasifikasicitra. Computer vision membantu pengenalan citra SIBI secara otomatis sehingga memudahkan orang normal berkomunikasi dengan orang tuli atau bisu. Pada penelitian sebelumnya belum ada yang melakukan perbandinganalgoritma klasifikasi machine learning dan deep learning untuk pengenalan SIBI. Perbandingan penting dilakukanuntuk melihat efektifitas tiap algoritma klasifikasi dalam hal performa klasifikasi dan waktu komputasi. Algoritmaklasifikasi machine learning memilikiwaktu komputasi lebih rendah sedangkan Deep learning memiliki performaklasifikasi lebih tinggi. Penelitian ini menganalisis time to performance dari algoritma machine learning dan deep learning dalam melakukan klasifikasi citra SIBI hurufA hingga Z. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional neural network (CNN) dengan transfer learning adalah tiga algorimtaklasifikasi populer yang dibandingkan dalam penelitian ini. Arsitektur transfer learning yang digunakan adalah Xception, ResNet50, VGG15, dan MobileNetV2 . Dari hasil penelitian yang dilakukan menggunakan 5 cross validation, CNN dengan arsitektur Xception memiliki nilaiF1 Score tertinggi yaitu 99,57% dengan waktu training rata-rata 1.387 detik. Sedangkan KNN dengan nilai K = 1 memiliki waktu training tercepat yaitu 0,03 detik dan memiliki nilai F1 Score 86,95% .  \nKata kunci: klasifikasi SIBI, KNN, SVM, CNN, machine learning, deep neural network  \nCOMPARISON ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS FOR IMAGE CLASSIFICATION OF INDONESIAN LANGUAGE  \nSIGNING SYSTEMS (SIBI)  \nAbstract  \nThe person who has a disorder such as deaf or dumb are unable to communicate verbally, which causes difficulties in communicating. They can only communicate through sign language, one of which is the Indonesian Language Sign System or Sistem Isyarat Bahasa Indonesia (SIBI). Sign Language Recognition is a complex classification problem to solve. Each sign language has its syntax and grammar. Computer vision is a technique used by computers to classify images. Computer vision helps automatically recognize SIBI images, making it easier for normal people to communicate with deaf or mute people. In previous studies, no one has compared machine learning and deep learning classification algorithms for the classification of SIBI. Therefore, a meaningful comparison is made to see each classification algorithm's effectiveness in classification performance and computation time. Machine learning classification algorithms have lower computation time, while Deep learning has higher classification performance. This study analyzes the time to performance of machine learning and deep learning algorithms in classifying SIBI images of letters A to Z. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional neural network (CNN) with transfer learning are three popular classification al","cbCail6u1mpBx3FU","https://ap.wps.com/l/cbCail6u1mpBx3FU","pdf",2282986,6,1,10,"Indonesian","id",113,"# PENDAHULUAN\n## Latar belakang kebutuhan pengenalan bahasa isyarat SIBI\n## Penelitian terdahulu terkait CNN dan transfer learning","[{\"question\":\"Mengapa pengenalan citra SIBI penting untuk komunikasi?\",\"answer\":\"Karena penderita gangguan pendengaran seperti tuli atau bisu kesulitan berkomunikasi verbal dan hanya dapat menggunakan bahasa isyarat. Computer vision membantu penerjemahan otomatis agar komunikasi dengan orang normal lebih mudah.\"},{\"question\":\"Metode apa saja yang dibandingkan dalam penelitian ini?\",\"answer\":\"Penelitian membandingkan K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN) dengan transfer learning.\"},{\"question\":\"Bagaimana hasil performa terbaik dan waktu training pada pengujian?\",\"answer\":\"CNN dengan arsitektur Xception menghasilkan F1 Score tertinggi 99,57% dengan waktu training rata-rata 1.387 detik. KNN dengan K=1 memiliki waktu training tercepat 0,03 detik, namun F1 Score 86,95%.\"}]","Analisis Perbandingan Algoritma Machine Learning Dan Deep Learning Untuk Klasifikasi Citra SIBI | PDF",1785726225,15,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparison-analysis-of-machine-learning-and-deep-learning-algorithms-for-sibi-image-classification","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/comparison-analysis-of-machine-learning-and-deep-learning-algorithms-for-sibi-image-classification/119770/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-12","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Mengapa pengenalan citra SIBI penting untuk komunikasi?","Question",{"text":77,"@type":78},"Karena penderita gangguan pendengaran seperti tuli atau bisu kesulitan berkomunikasi verbal dan hanya dapat menggunakan bahasa isyarat. Computer vision membantu penerjemahan otomatis agar komunikasi dengan orang normal lebih mudah.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Metode apa saja yang dibandingkan dalam penelitian ini?",{"text":82,"@type":78},"Penelitian membandingkan K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN) dengan transfer learning.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana hasil performa terbaik dan waktu training pada pengujian?",{"text":86,"@type":78},"CNN dengan arsitektur Xception menghasilkan F1 Score tertinggi 99,57% dengan waktu training rata-rata 1.387 detik. KNN dengan K=1 memiliki waktu training tercepat 0,03 detik, namun F1 Score 86,95%.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]