SMART ATTENDANCE SYSTEM MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

RIFKI, MUHAMMAD and Supardi, Julian and Arsalan, Osvari (2022) SMART ATTENDANCE SYSTEM MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Face clasification using the convolutional neural networks method has become an interesting research topic in recent years. development method using convolutional neural networks and facenet architecture. Research by collecting primary data from direct observation, in completing this research is of course the FaceNet model. The FaceNet model that has been built is based on the configuration that has been trained using training data of 7,600 facial image data. The results of the FaceNet model training process for training data are 100,000 for accuracy and 100,000 for test. this proves the level of accuracy of this architecture is very good in classifying faces. to classify images for presence presence using the Convolutional Neural Network model using the FaceNet architecture and gives very good classification results. The results of the FaceNet architectural model training obtained an accuracy value of 100% in the model training process. The results of the testing process on the test data set with the threshold configuration obtained an F1 score of 100% for each test data. The results of the FaceNet model training process on training data are 100,000 for accuracy and 100,000 for test. this proves the level of accuracy of this architecture is very good in classifying faces. The results of the FaceNet architectural model training obtained an accuracy value of 100% in the model training process. The results of the testing process on the test data set with the threshold configuration obtained an F1 score of 100% for each test data. The results of the FaceNet architectural model training obtained an accuracy value of 100% in the model training process. The results of the testing process on the test data set with the threshold configuration obtained an F1 score of 100% for each test data.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Facenet, CNN, Smart Attendance System, pengenalan wajah
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Rifki
Date Deposited:	26 Jan 2023 07:35
Last Modified:	26 Jan 2023 07:35
URI:	http://repository.unsri.ac.id/id/eprint/87595

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