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Sistem mampu melakukan deteksi gerak, deteksi manusia, serta pengenalan postur tubuh dasar, disertai notifikasi otomatis sebagai respons awal. Implementasi memanfaatkan GMM untuk deteksi gerak, AdaBoost untuk deteksi wajah, serta PoseNet dan rule-based system untuk klasifikasi postur. Pengujian dilakukan pada dataset rekaman CCTV simulatif dengan skenario aktivitas kampus. Hasil awal menunjukkan akurasi deteksi yang cukup tinggi, sehingga berpotensi menjadi dasar sistem keamanan yang lebih responsif dan otomatis.","Print ISSN : 2774 – 8928Online ISSN : 2774 – 8510  \n# PROTOTYPE SISTEM PEMANTAUAN CERDAS BERBASISMACHINE LEARNING UNTUK KEAMANAN UNIVERSITASRAHARJA\n\nSendy Zul Friandi 1,Mohamad Ifran Sanni 2 , Irma Marladewi 3, Indra Setiawan 4  \nUniversitas Raharja, Tangerang, Indonesiae-mail: 1 sendy@raharja.info , 2ifran@raharja.info , 3irma.marladewi@raharja.info ,4indra.s@raharja.info  \n## Abstrak\n\nPenelitian ini bertujuan untuk merancang dan mengembangkan sebuahprototype sistem  \npemantauan cerdas berbasis kamera CCTV menggunakan teknologi machine learning, yangditujukan untuk mendukung keamanan di lingkungan universitas raharja. Sistem ini dirancanguntuk mampu melakukan deteksigerak, deteksi manusia, sertapengenalan postur tubuh, danmemberikan notifikasi otomatis sebagai bentuk respons awal terhadap aktivitas tertentu. Dalamprototipe ini, digunakan algoritma Gaussian Mixture Model (GMM) untukdeteksigerak,AdaBoost untuk deteksi wajah, serta kombinasi PoseNet dan Rule-Based System untukmengenalipostur tubuh dasar. Pengujian dilakukan di lingkungan laboratorium menggunakandataset rekaman CCTV simulatifyang menyerupai aktivitas di universitas raharja. Hasil awalmenunjukkan bahwa sistem mampu melakukan deteksi dengan akurasi yang cukup tinggi,sehingga dapat menjadi dasar untukpengembangan sistem keamanan universitas raharja yanglebih responsif dan otomatis di masa depan.  \nKata kunci: pemantauan cerdas, machine learning, CCTV, deteksigerak, pengenalan postur  \n## Abstract\n\nThis research aims to design and develop a prototype intelligent surveillance system based onCCTV cameras using machine learning technology, intended to support security at RaharjaUniversity. The system is designed to perform motion detection, human detection, andposturerecognition, providing automatic notifications as an initial response to specific activities. Theprototype uses the Gaussian Mixture Model (GMM) algorithm for motion detection, AdaBoostfor face detection, and a combination ofPoseNet and a Rule-Based Systemfor basic posturerecognition. Testing was conducted in a laboratory environment using a simulated CCTVfootage dataset that mimics the activities at Raharja University. Initial results indicate that thesystem is capable of detecting with relatively high accuracy, thus providing the basis for thedevelopment of a more responsive and automated security system at Raharja University in thefuture.  \nKeywords: intelligent surveillance, machine learning, CCTV, motion detection, posturerecognition  \n120  \nVol. 5 No. 2 - Agustus 2025  \nPrint ISSN : 2774 – 8928  \nOnline ISSN : 2774 – 8510  \n## 1. PENDAHULUAN\n\nKeamanan merupakan aspek penting dalam lingkungan pendidikan, termasuk diuniversitas raharja Seiring berkembangnya teknologi, sistem pemantauan berbasis kameraCCTV menjadi salah satu solusi populer dalam meningkatkan keamanan. Namun, sistem CCTVkonvensional masih bergantung pada pengawasan manusia secara terus-menerus, yang kurangefisien dan rawan terlewat.  \nMachine Learning (ML) menawarkan pendekatan baru dalam pengembangan sistempengawasan cerdas, dengan kemampuan untuk menganalisis video secara otomatis danmendeteksikejadian mencurigakan atau tidak biasa. Penelitian ini berfokus pada pengembangansebuah prototype sistem pemantauan cerdas berbasis machine learning, yang dirancang khususuntuk mendukung keamanan di lingkungan universitas raharja.  \nMeskipun sistem belum diimplementasikan secara nyata di lapangan, prototipe inidiuji dilaboratorium menggunakan data rekaman video CCTV yang disimulasikan berdasarkanskenario umum yang terjadidi universitas raharja, seperti aktivitas mahasiswa di lorong, ruangkelas, dan area terbuka. Sistem yang dikembangkan mengintegrasikan beberapa algoritma ML,yaitu GMM untuk deteksi gerak, AdaBoost untuk deteksi wajah, dan PoseNet + Rule-BasedSystem untuk pengenalan postur tubuh.  \n121  \nVol. 5 No. 2 - Agustus 2025  \nPrint ISSN : 2774 – 8928Online ISSN : 2774 – 8510  \n## 2. METODE PENGABDIAN\n\nPenelitian ini menggunakan pendekatan p","cbCailHOoMZiofd1","https://ap.wps.com/l/cbCailHOoMZiofd1","pdf",415602,4,1,5,"Indonesian","id",113,"# Abstrak\n# Pendahuluan\n## Latar belakang\n## Fokus penelitian\n# Metode Pengabdian\n## Pengumpulan data\n## Pelatihan model\n## Implementasi prototipe\n## Pengujian prototipe\n# Hasil dan Pembahasan\n## Deteksi gerak (GMM)\n## Deteksi wajah (AdaBoost)\n## Pengenalan postur tubuh (PoseNet + Rule-Based)","[{\"question\":\"Apa tujuan utama penelitian pada dokumen ini?\",\"answer\":\"Merancang dan mengembangkan prototipe sistem pemantauan cerdas berbasis CCTV dengan machine learning untuk mendukung keamanan di Universitas Raharja.\"},{\"question\":\"Algoritma apa saja yang digunakan untuk tiap kemampuan deteksi?\",\"answer\":\"GMM digunakan untuk deteksi gerak, AdaBoost untuk deteksi wajah, serta PoseNet dikombinasikan rule-based system untuk pengenalan postur tubuh dasar.\"},{\"question\":\"Bagaimana pengujian prototipe dilakukan dan apa hasil awalnya?\",\"answer\":\"Prototipe diuji di lingkungan laboratorium menggunakan dataset rekaman CCTV simulatif yang meniru aktivitas kampus. Hasil awal menunjukkan performa cukup tinggi: deteksi gerak 85%, deteksi wajah 80%, dan klasifikasi postur 72%.\"}]","PROTOTYPE SISTEM PEMANTAUAN CERDAS BERBASIS MACHINE LEARNING UNTUK KEAMANAN UNIVERSITASRAHARJA - penelitian dan pengujian prototipe | PDF",1785735773,8,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"prototype-intelligent-surveillance-system-based-on-machine-learning-for-raharja-university-security-prototype-research-and-testing","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/prototype-intelligent-surveillance-system-based-on-machine-learning-for-raharja-university-security-prototype-research-and-testing/121463/",{"url":53,"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","2026-08-17","2026-08-03",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},"Apa tujuan utama penelitian pada dokumen ini?","Question",{"text":76,"@type":77},"Merancang dan mengembangkan prototipe sistem pemantauan cerdas berbasis CCTV dengan machine learning untuk mendukung keamanan di Universitas Raharja.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Algoritma apa saja yang digunakan untuk tiap kemampuan deteksi?",{"text":81,"@type":77},"GMM digunakan untuk deteksi gerak, AdaBoost untuk deteksi wajah, serta PoseNet dikombinasikan rule-based system untuk pengenalan postur tubuh dasar.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana pengujian prototipe dilakukan dan apa hasil awalnya?",{"text":85,"@type":77},"Prototipe diuji di lingkungan laboratorium menggunakan dataset rekaman CCTV simulatif yang meniru aktivitas kampus. Hasil awal menunjukkan performa cukup tinggi: deteksi gerak 85%, deteksi wajah 80%, dan klasifikasi postur 72%.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]