IMPLEMENTASI SISTEM DETEKSI PEMAKAIAN SAFETY EQUIPMENT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK PADA MODEL MACHINE LEARNING

Muhammad Hafiyyan Fadhil Riezthia, . (2024) IMPLEMENTASI SISTEM DETEKSI PEMAKAIAN SAFETY EQUIPMENT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK PADA MODEL MACHINE LEARNING. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

A company or agency is required to implement an OHS (Occupational Health and Safety) policy in its work area. One aspect of this policy is the safety factor where the use of PPE (Personal Protective Equipment) on workers needs to be ensured. Workers who do not use PPE have the potential for accidents that can occur. Therefore, to ensure the use of PPE can be assisted by technology that can monitor and ensure workers to always use PPE. So we need a program that can be used to meet these needs. This process will be implemented using a Machine Learning model that will detect and classify the image processing of workers who are required to wear security equipment. Convolutional Neural Network (CNN) with its application YOLO (You Only Look Once) will be used to carry out classification and detection by inputting images to find out whether workers are wearing safety equipment or not. The implementation that will be included is still the image of security equipment called Personal Protective Equipment (PPE). The results of this implementation will provide benefits for many companies or agencies that have work spaces that require workers to wear PPE (Personal Protective Equipment).

Item Type:	Thesis (Skripsi)
Additional Information:	[No. Panggil : 1910511064] [Pembimbing : Rio Wirawan] [Penguji 1 : Widya Cholil] [Penguji 2 : Henki Bayu Seta]
Uncontrolled Keywords:	CNN (Convolutional Neural Network), Machine Learning, PPE (Personal Protective Equipment), classification, detection, YOLO (You Only Look Once) and image processing.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User:	Muhammad Hafiyyan Fadhil Riezthia
Date Deposited:	19 Feb 2024 04:55
Last Modified:	19 Feb 2024 08:01
URI:	http://repository.upnvj.ac.id/id/eprint/28915

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