[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125906-id":3,"doc-seo-125906-113":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},125906,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",52,"Teknologi","DESMOCAM (DETECTION SMOKING CAMERA) - IOT DENGAN IMPLEMENTASI MACHINE LEARNING UNTUK DETEKSI PEROKOK AKTIF GUNA MENDUKUNG SMART CITY DI INDONESIA","Rokok sebagai zat adiktif menimbulkan dampak kesehatan dan kerugian ekonomi yang besar, sekaligus memicu kematian aktif maupun perokok pasif. Di Indonesia, regulasi kawasan tanpa rokok ditargetkan mencapai 100%, namun pelaksanaannya masih belum merata sehingga memerlukan pemantauan berkelanjutan berbasis teknologi. DesMoCam dirancang sebagai sistem deteksi perokok menggunakan IoT untuk menegaskan KTR dalam mendukung Smart City. Sistem memanfaatkan model InceptionResNet2, Raspberry Pi, dan ESP32-CAM untuk akuisisi gambar dan peringatan.","DESMOCAM (DETECTION SMOKING CAMERA): INTEGRATION OF IOTAND MACHINE LEARNING FOR ACTIVE SMOKER DETECTION TO SUPPORT SMART  \nCITIES IN INDONESIA  \nAnnas Abdillah*1, Balqist K. Nayu2, Susi Setianingsih3, Galih B. Hidayat4, Tuhfa R. Ahmad5  \n1,4Electrical Engineering, Faculty of Engineering, Universitas Jenderal Soedirman, Indonesia  \n2,5Public Health, Faculty of Health, Universitas Jenderal Soedirman, Indonesia  \n3Informatics, Faculty of Engineering, Universitas Jenderal Soedirman, Indonesia [Email:](Email:1annas.abdillah.aa@gmail.com)[1](Email:1annas.abdillah.aa@gmail.com)[annas.abdillah.aa@gmail.com](Email:1annas.abdillah.aa@gmail.com), [2](2balqistkha.nayu@gmail.com)[balqistkha.nayu@gmail.com](2balqistkha.nayu@gmail.com), [3](3susisetia542@gmail.com)[susisetia542@gmail.com](3susisetia542@gmail.com),  \n[4](4galihbaskorohidayat@gmail.com)[galihbaskorohidayat@gmail.com](4galihbaskorohidayat@gmail.com), [5](5tuhfaziya@gmail.com)[tuhfaziya@gmail.com](5tuhfaziya@gmail.com)  \n(Article received: May 28, 2024; Revision: June 7, 2024; published: August 01, 2024)  \nAbstract  \nCigarettes are an addictive substance that kills around 8 million people every year, as of 2022 there will be around 8,67 million deaths in the world caused by cigarettes and other tobacco products with resulting economic losses of around 2 trillion USD. Efforts to reduce losses due to smoking in Indonesia have been implemented through various regulations and rules that have been established, such as Law Number 36 of 2009 Article 115 concerning non-smoking areas. The target for non-smoking areas (NSA) regulations in Indonesia will reach 100% by 2023. However, currently, only 86% of regions haveNSA regulations and must continue to monitor and evaluate through regulations set by the government. One solution to emphasize non-smoking areas with the latest technology connections to support Smart City is a smoke detection system using IoT. DesMoCam (Detection Smoking Camera) applies the latest machine learning model, InceptionResNet2, which has high accuracy and has the ability to detect smokers precisely in a Non-Smoking Area (NSA). DesMoCam uses a Raspberry Pi with ESP32-CAM to capture situations in a smoking-free room and warnings through the speaker. Machine learning modeling includes data acquisition with smoking and non-smoking images, data preprocessing, two-way modeling with and without a freeze layer, and analysis of model results. The InceptionResnet2 model used for image identification and classification, achieved an accuracy of 92.75%.  \nKeywords: IoT, Machine Learning, Smoke Detection.  \nDESMOCAM (DETECTION SMOKING CAMERA): IOT DENGAN IMPLEMENTASI MACHINE LEARNING UNTUK DETEKSI PEROKOK AKTIF GUNA MENDUKUNG SMART CITY DI INDONESIA  \nAbstrak  \nRokok, benda adiktif yang membunuh sekitar 8 juta manusia setiap tahunnya, per 2022 tercatat sekitar 8.67 jutakematian dunia yang disebabkan oleh rokok dan produk tembakau lainnya dengan kerugian ekonomi yang dihasilkan sekitar 2 triliun USD. Usaha pengurangan kerugian akibat rokok di Indonesia telah dilaksanakanmelalui berbagai regulasi dan peraturan yang telah dibentuk seperti Undang-Undang Nomor 36 Tahun 2009 Pasal 115 mengenai kawasan tanpa rokok. Target aturan Kawasan Tanpa Rokok (KTR) di Indonesia akan mencapai 100% pada tahun 2023, namun saat ini hanya terdapat 86% daerah yang memiliki aturan KTR dan harus terusdiperluas, dimonitoring dan dievaluasi melalui regulasi yang telah pemerintah tetapkan. Salah satu upaya solusi untuk mempertegas kawasan tanpa rokok dengan penggabungan teknologi terkini demi mendukung Smart City yaitu dengan sistem deteksi perokok menggunakan IOT. DesMoCam (Detection Smoking Camera) memanfaatkan model machine learning terbaru yakni InceptionResNet2 yang memiliki akurasi tinggi untuk mendeteksi perokok secara tepat di kawasan tanpa rokok serta pemanfaatan Raspberry pi dengan ESP32-CAM untuk mengambilgambar kondisi di ruangan kawasan tanpa rokok. Gambar tersebut diproses di Node Red serta pe","cbCaiknhbPtCKchU","https://ap.wps.com/l/cbCaiknhbPtCKchU","pdf",846940,5,1,9,"Indonesian","id",113,"# PENDAHULUAN\n## Dampak rokok dan urgensi kawasan tanpa rokok\n## Kesenjangan implementasi regulasi dan kebutuhan monitoring\n## Konsep sistem deteksi berbasis IoT dan machine learning","[{\"question\":\"Apa tujuan utama DesMoCam dalam mendukung Smart City?\",\"answer\":\"DesMoCam bertujuan menegaskan kawasan tanpa rokok melalui sistem deteksi perokok berbasis IoT, sehingga pemantauan dan respons dapat dilakukan secara lebih efektif.\"},{\"question\":\"Teknologi apa yang digunakan untuk menangkap dan memproses kondisi ruangan?\",\"answer\":\"DesMoCam menggunakan Raspberry Pi dengan ESP32-CAM untuk menangkap gambar pada ruangan bebas rokok, lalu gambar diproses untuk menghasilkan deteksi.\"},{\"question\":\"Bagaimana kinerja model machine learning pada DesMoCam?\",\"answer\":\"Model InceptionResNet2 digunakan untuk identifikasi dan klasifikasi gambar, dengan capaian akurasi 92,75%.\"}]","DESMOCAM (DETECTION SMOKING CAMERA) - IOT DENGAN IMPLEMENTASI MACHINE LEARNING UNTUK DETEKSI PEROKOK AKTIF GUNA MENDUKUNG SMART CITY DI INDONESIA | PDF",1785901966,14,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"desmocam-detection-smoking-camera-iot-with-machine-learning-implementation-for-active-smoker-detection-to-support-smart-city-in-indonesia","",{"@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/teknologi/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/desmocam-detection-smoking-camera-iot-with-machine-learning-implementation-for-active-smoker-detection-to-support-smart-city-in-indonesia/125906/",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","2026-08-15","2026-08-05",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},"Apa tujuan utama DesMoCam dalam mendukung Smart City?","Question",{"text":77,"@type":78},"DesMoCam bertujuan menegaskan kawasan tanpa rokok melalui sistem deteksi perokok berbasis IoT, sehingga pemantauan dan respons dapat dilakukan secara lebih efektif.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Teknologi apa yang digunakan untuk menangkap dan memproses kondisi ruangan?",{"text":82,"@type":78},"DesMoCam menggunakan Raspberry Pi dengan ESP32-CAM untuk menangkap gambar pada ruangan bebas rokok, lalu gambar diproses untuk menghasilkan deteksi.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana kinerja model machine learning pada DesMoCam?",{"text":86,"@type":78},"Model InceptionResNet2 digunakan untuk identifikasi dan klasifikasi gambar, dengan capaian akurasi 92,75%.","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},[95,100,104,108,112,116,120,124,126,130,134],{"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":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":98,"slug":119},54,"Penelitian & Laporan","research-report",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":98,"slug":123},49,"Sastra","literature",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":125},"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"]