Iqbal, Rifqi Muhammad (2022) Human Movement Detection System when Coughing Using Convolutional Neural Network(CNN). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.
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Abstract
Convolutional Neural Network (CNN) is a machine learning method from the development of Multi Layer Perceptron (MLP) which is designed to process two-dimensional data, so that the linear operations and weight parameters on CNN are different. Cough Detection Using Convolutional Neural Network (CNN) is one of the fields of artificial intelligence. Cough is an effort to defend the lungs against various existing stimuli and is a physiological reflex that protects the lungs from mechanical, chemical and temperature trauma. Not a few people experience this, because this cough is a very common disease experienced by humans. There are many ways people express themselves when they cough, namely by the actions they take. In this study, a system will be created that can detect the movement or actions of human objects when coughing by using the CNN method and using digital images to detect human movement in video. The results of the research for epochs 150 get the model for the best accuracy, the accuracy value is 87.62%. For the test results with 41% accuracy Keywords: Convolutional Neural Network (CNN), Human movement when coughing, Video, accuracy epochs.
Item Type: | Thesis (Undergraduate) |
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Uncontrolled Keywords: | Convolutional Neural Network (CNN), Gerakan manusia saat batuk, Video,epochs, akurasi |
Subjects: | T Technology > T Technology (General) |
Divisions: | Fakultas Teknik > Program Studi Teknik Informatika |
Depositing User: | 1461700137 Rifqi Muhammad Iqbal |
Date Deposited: | 20 Apr 2022 05:57 |
Last Modified: | 20 Apr 2022 05:57 |
URI: | http://repository.untag-sby.ac.id/id/eprint/14597 |
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