PENGENALAN WAJAH TAMPAK SAMPING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

RAIHAN, KEMAS FACHIR and Fachrurrozi, Muhammad and Rachmatullah, Muhammad Naufal (2023) PENGENALAN WAJAH TAMPAK SAMPING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

	Text RAMA_55201_09021281823068.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_55201_09021281823068_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (2MB)
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (386kB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (400kB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (131kB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_06.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (66kB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_07_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (85kB) | Request a copy
	Text RAMA_55201_09021281823068_ 0222058001_0001129204_08_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (125kB) | Request a copy

Abstract

Face is one part of the human body that is used to recognize a person. Faces are used to recognize different people because faces have the highest differences in each person so they are often used in human facial recognition automation systems. In this study, the object used to recognize faces is the side face using the Convolutional Neural Network method. The data used in this study was obtained from DigiFace-1M as many as 100 people with a total of 25-40 facial images of each person's side. The total data collected was 3623 images and data were grouped in folders based on their labels. Based on the research that has been done, facial recognition of side views using the Convolutional Neural Network method was successfully developed, which resulted in the highest accuracy of 85.64%. These results were obtained using a model with the ResNet50 architecture. These results were obtained using a model with the ResNet50 architecture. This is because the data is quite limited to train the Convolutional Neural Network model. Keywords: Face Recognition, Side face, Convolutional Neural Network

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Face Recognition, Side face, Convolutional Neural Network
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Kemas Fachir Raihan
Date Deposited:	08 Aug 2023 02:12
Last Modified:	08 Aug 2023 02:12
URI:	http://repository.unsri.ac.id/id/eprint/126192

Actions (login required)

View Item