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dc.contributor.advisorFauzi, Rahmad
dc.contributor.authorPandiangan, Dolli
dc.date.accessioned2025-05-20T02:02:37Z
dc.date.available2025-05-20T02:02:37Z
dc.date.issued2024
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/103905
dc.description.abstractPersonal Protective Equipment plays a big role in minimising the level of injury while working. Because a worker accidentally does not wear personal protective equipment, to reduce it, a prototype system is made to detect the use of personal protective equipment in the form of a dataset of a collection of images that are trained using the Convolutional Neural Network (CNN) method with the Yolov8 algorithm which is part of deeplearning, it is obtained from research systems that can detect personal protective equipment from a Webcam camera with an accuracy rate from the experiment reaching 97% so that the system has shown good performance.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectDeeplearningen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectYolov8Webcamen_US
dc.subjectpersonal protective equipmenten_US
dc.titleImplementasi Convolutional Neural Network dalam Mendeteksi Penggunaan Alat Pelindung Diri sebagai Keamanan dan Keselamatan Kerjaen_US
dc.title.alternativeImplementation of Convolutional Neural Network in Detecting the Use of Personal Protective Equipment as Work Safety and Securityen_US
dc.typeThesisen_US
dc.identifier.nimNIM170402165
dc.identifier.nidnNIDN0024046903
dc.identifier.kodeprodiKODEPRODI20201#Teknik Elektro
dc.description.pages77 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US
dc.subject.sdgsSDGs 9. Industry Innovation And Infrastructureen_US


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