[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121605-en":3,"doc-seo-121605-105":30,"detail-sidebar-cat-0-en-105":91},{"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":11,"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},121605,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Best Machine Learning Model for Face Recognition in Home Security Application - Paper","Particularly since the COVID-19 outbreak, Indonesia has seen a year-on-year rise in criminal prosecutions, increasing the need for stronger home security. The paper uses face recognition as a core security mechanism, covering face detection, segmentation, and recognition to prevent misclassification and improve system reliability. Models are optimized via Grid Search CV across multiple machine-learning methods, reaching at least 90% accuracy. SVM delivers the best result with 100% accuracy, offering a simpler alternative to deep learning for real deployments.","Best Machine Learning Model for Face Recognition in Home Security Application  \nIstiqomah1, Faqih Alam2 dan Achmad Rizal3,  \nSitasi: Istiqomah; Alam, F.; dan Rizal, A. (2021) . Best Machine Learning Model for Face Recognition in Home Security Application . JTIM: Jurnal Teknologi Informasi Dan Multimedia, 4(4), 300-307.  \n[https://doi.org/10.35746/jtim.v4i4.306](https://doi.org/10.35746/jtim.v4i4.306)  \nCopyright: © 2023 by the authors. This work is licensed under a Creative Commons AttributionShareAlike 4.0 International License.([https://creativecommons.org/license](https://creativecommons.org/license)[s/by-sa/4.0/](s/by-sa/4.0/)) .  \n1 Telkom University; [Istiqomah@telkomuniversity.ac.id](Istiqomah@telkomuniversity.ac.id)  \n2 Telkom University; [faqihalam@student.telkomuniversity.ac.id](faqihalam@student.telkomuniversity.ac.id)  \n3 Telkom University; [achmadrizal@telkomuniversity.ac.id](achmadrizal@telkomuniversity.ac.id)  \n* Korespondensi: [Istiqomah@telkomuniversity.ac.id](Istiqomah@telkomuniversity.ac.id)  \nAbstract: Particularly since the COVID-19 outbreak, Indonesia has seen an annual surge in criminal prosecutions. To increase home security, many technological advances have been made. Face recognition served as the main form of security for almost all of them. Face detection, face segmentation, and face recognition are the three steps in the face recognition process. To avoid misclassification and increase system dependability, accurate recognition of faces becomes crucial in security systems. The optimization tool Grid Search CV produces using a number of machine learning methods that are proposed. Each machine learning has been created using its best model and has attained accuracy levels of at least 90%. The most effective strategy is SVM, which has 100% accuracy rates. A technique for choosing the best model is an alternative. Machine learning model can be alternative implementation in real system, that can proposed more simple machine learning model than deep learning.  \nKeywords: Face Recognition, Machine Learning, Home Security  \nAbstrak: Terutama sejak wabah COVID-19, Indonesia telah mengalami lonjakan tuntutan pidana setiap tahunnya. Untuk meningkatkan keamanan rumah, banyak kemajuan teknologi telah dilakukan. Pengenalan wajah berfungsi sebagai bentuk keamanan utama untuk hampir semuanya. Deteksi wajah, segmentasi wajah, dan pengenalan wajah adalah tiga langkah dalam proses pengenalan wajah. Untuk menghindari kesalahan klasifikasi dan meningkatkan keandalan sistem, pengenalan wajah yang akurat menjadi sangat penting dalam sistem keamanan. Alat pengoptimalan Grid Search CV menghasilkan menggunakan sejumlah metode pembelajaran mesin yang diusulkan. Setiap pembelajaran mesin telah dibuat menggunakan model terbaiknya dan telah mencapai tingkat akurasi minimal 90%. Strategi yang paling efektif adalah SVM, yang memiliki tingkatakurasi 100%. Model pembelajaran mesin dapat menjadi alternatif penerapan dalam sistem nyata, yang dapat mengusulkan model machine learning yang lebih sederhana daripada deep learning.  \nKata kunci: Pengenalan Wajah, Machine Learning, Home Security.  \n1. Introduction  \nHome security is a very important issue lately due to the increase criminal after the Covid-19 pandemic. Criminal cases have increased by 7.3% in 2022 compared to previous year in Indonesia[1], so further prevention is needed to solve this problem. Many things have been done to improve home security, from adding monitoring systems such as  \nCCTV[2]–[4], until developing smart lock doors to monitor who enters the house[5]–[8]. All these systems are supported by face recognition technology to monitor who is in the house.  \nThere are three processes to do face recognition, face detection, face segmentation, and face recognition [9]. The first step, the face detection process, must do, with searching for the face component in every pixel in the image. After that, to get the face feature, face segmentation is executed. That feature i","cbCaigXZTNIG980A","https://ap.wps.com/l/cbCaigXZTNIG980A","pdf",670860,2,1,"English","en",105,"# Introduction\n## Face recognition process and accuracy needs\n## Prior work and motivation for alternative models\n# Proposed Method\n## System pipeline with Raspberry Pi camera and Haar Cascade\n## Classification using optimized machine-learning models","[{\"question\":\"What are the main steps in the face recognition process used in the study?\",\"answer\":\"The study describes three steps: face detection, face segmentation, and face recognition. These steps help extract and use face features for classification in a security system.\"},{\"question\":\"How does the study optimize machine learning models?\",\"answer\":\"It applies the Grid Search CV optimization technique to tune the best hyperparameters for each proposed machine learning model within the scikit-learn workflow.\"},{\"question\":\"Which machine learning model achieves the highest accuracy, and what is the reported value?\",\"answer\":\"SVM is identified as the most effective strategy, achieving 100% accuracy in the study's comparison results.\"}]","Best Machine Learning Model for Face Recognition in Home Security Application - Paper | PDF",1785736442,20,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"best-machine-learning-model-for-face-recognition-in-home-security-application-paper","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/best-machine-learning-model-for-face-recognition-in-home-security-application-paper/121605/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What are the main steps in the face recognition process used in the study?","Question",{"text":75,"@type":76},"The study describes three steps: face detection, face segmentation, and face recognition. 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