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PENGEMBANGAN SISTEM BASIS DATA DAN PELAPORAN PADA SMART ATTENDANCE SYSTEM MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

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Face detection is an interesting area of research in the application of computer vision and pattern recognition, especially during the last few years. in his research to filter selfie face images in search results based on hashtags on Instagram by combining web data extraction techniques and human face detection techniques using the Haar Cascade method and the experimental results show that the method applied produces an accuracy value of 71.48% for detecting human faces. Based on the results of human face detection, the Haar Cascade method can filter selfie facial images with an accuracy value of 64.6%. However, the face detection used by researchers was made only to meet system testing criteria. Therefore, it is necessary to apply face detection to extract a person's facial features which will be used as sample data for facial classification using CNN which is then applied to the Smart Attendance System so that this presence system is better and more perfect. The CNN architecture used is FaceNet. The FaceNet model that has been built is based on a configuration that has been trained using training data of 7,500 facial image data. The results of the FaceNet model training process on training data are 100,000 for accuracy and 100,000 for test. This proves that facial feature extracts using Haarcascade can be used as input for the FaceNet model.

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2307001003T87608T876082023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T876082023
Publisher
Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xviii, 116 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.660 7
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Metode Transmisi Data
Prodi Teknik Informatika
Specific Detail Info
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Statement of Responsibility
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