[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-128154-113":53,"doc-detail-128154-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","optimization-of-air-conditioning-system-with-machine-learning-method-integrated-with-internet-of-things","OPTIMASI SISTEM PENGKONDISIAN UDARA DENGAN METODE MACHINE LEARNING TERINTEGRASI INTERNET OF THINGS","","Penelitian ini berfokus pada optimasi sistem pengkondisian udara melalui integrasi metode machine learning dan Internet of Things (IoT). Hasil penelitian menunjukkan bahwa optimasi sistem pengendalian pengkondisian udara berbasis IoT berhasil dilakukan, menghasilkan sistem yang efisien dalam mengatur kenyamanan termal di ruangan RSTA-B1 dengan otomatisasi. Integrasi machine learning, khususnya Artificial Neural Network (ANN), menghasilkan sistem pengkondisian udara yang nyaman dan efisien sesuai standar kenyamanan termal ASHRAE-55. Penghematan energi listrik yang signifikan terukur selama tiga hari pengujian, dengan penghematan masing-masing sebesar 4154,06 watt pada hari pertama, 1887,469 watt pada hari kedua, dan 3293,033 watt pada hari ketiga. Saran untuk penelitian lanjutan mencakup pertimbangan pengaruh panas dari individu dengan temperatur tinggi atau kondisi sakit. Sistem yang dioptimasi ini menawarkan solusi efisien untuk manajemen termal ruangan dan penghematan energi.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/optimization-of-air-conditioning-system-with-machine-learning-method-integrated-with-internet-of-things/128154/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/optimization-of-air-conditioning-system-with-machine-learning-method-integrated-with-internet-of-things/128154.png","ImageObject",300,407,{"name":89,"@type":90},"River Wang","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-18","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",7,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Bagaimana optimasi sistem pengkondisian udara berhasil dilakukan?","Question",{"text":110,"@type":111},"Optimasi sistem pengkondisian udara berhasil dilakukan dengan mengintegrasikan metode machine learning, khususnya Artificial Neural Network (ANN), dengan sistem berbasis Internet of Things (IoT). Hasilnya adalah sistem yang efisien dalam mengatur kenyamanan termal ruangan secara otomatis.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Apakah sistem yang dioptimasi memenuhi standar kenyamanan termal?",{"text":115,"@type":111},"Ya, sistem pengkondisian udara yang diintegrasikan dengan machine learning dan IoT ini diklaim nyaman dan efisien serta memenuhi standar kenyamanan termal ASHRAE-55.",{"name":117,"@type":108,"acceptedAnswer":118},"Berapa besar penghematan energi listrik yang dicapai?",{"text":119,"@type":111},"Penghematan energi listrik yang terukur selama tiga hari pengujian sistem yang dioptimasi menunjukkan angka 4154,06 watt pada hari pertama, 1887,469 watt pada hari kedua, dan 3293,033 watt pada hari ketiga dibandingkan dengan sistem yang tidak dioptimasi.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},128154,1785945138,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":71,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":127,"read_time":74},549768072016,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","BAB V  \nKESIMPULAN  \n5.1 Kesimpulan  \nBerdasarkan hasil penelitian yang telah dilakukan, maka dapat diambil kesimpulan sebagai berikut :  \n1. Optimasi sistem pengendalian pengkondisian udara berbasis IoT berhasil dilakukan. Hasil dari optimasi berupa sistem yang efisien bekerja mengatur kenyamanan termal di ruangan RSTA-B1 dengan sistem pengkondisianudara yang diotomasi.  \n2. Sistem pengkondisian udara berbasis IoT dioptimasi dengan mengintegrasikan machine learning ANN. Pengintegrasian machine learning menghasilkan sistem pengkondisian udara yang nyaman danefisien sesuai standar kenyamanan thermal ASHRAE-55.  \n3. Hasil optimasi sistem pengkondisian udara berbasis IoT, berhasil menghemat penggunaan energi listrik yang telah diukur sebanyak 3 hari pengujian penggunaan sistem yang dioptimasi dan tidak dioptimasi. Hasil pengujian menunjukkan penghematan energi sebesar 4154,06 watt pada hari ke-1, 1887,469 watt pada hari ke-2, 3293,033 watt dan pada hari ke-3 .  \n5.2 Saran  \nBerdasarkan penelitian yang sudah dilakukan, maka ada beberapa saran dari penulis yaitu :  \n1. Penelitian lanjutan dapat dilakukan dengan mempertimbangkan pengaruhpanas yang berasal dari individu yang memiliki temperatur tinggi ataudalam keadaan sakit.","cbCaieX5amYFkAlb","https://ap.wps.com/l/cbCaieX5amYFkAlb","pdf",34339,"Indonesian","# BAB V\n# KESIMPULAN\n## 5.1 Kesimpulan\n## 5.2 Saran","[{\"question\":\"Bagaimana optimasi sistem pengkondisian udara berhasil dilakukan?\",\"answer\":\"Optimasi sistem pengkondisian udara berhasil dilakukan dengan mengintegrasikan metode machine learning, khususnya Artificial Neural Network (ANN), dengan sistem berbasis Internet of Things (IoT). Hasilnya adalah sistem yang efisien dalam mengatur kenyamanan termal ruangan secara otomatis.\"},{\"question\":\"Apakah sistem yang dioptimasi memenuhi standar kenyamanan termal?\",\"answer\":\"Ya, sistem pengkondisian udara yang diintegrasikan dengan machine learning dan IoT ini diklaim nyaman dan efisien serta memenuhi standar kenyamanan termal ASHRAE-55.\"},{\"question\":\"Berapa besar penghematan energi listrik yang dicapai?\",\"answer\":\"Penghematan energi listrik yang terukur selama tiga hari pengujian sistem yang dioptimasi menunjukkan angka 4154,06 watt pada hari pertama, 1887,469 watt pada hari kedua, dan 3293,033 watt pada hari ketiga dibandingkan dengan sistem yang tidak dioptimasi.\"}]","OPTIMASI SISTEM PENGKONDISIAN UDARA DENGAN METODE MACHINE LEARNING TERINTEGRASI INTERNET OF THINGS | PDF"]