Sistem Manajemen Absensi Dengan Deteksi Objek Ktm Menggunakan Metode CNN (Convolution Neural Network)

Rizki, Dwi Putranto (2022) Sistem Manajemen Absensi Dengan Deteksi Objek Ktm Menggunakan Metode CNN (Convolution Neural Network). Project Report. Institut Telkom Telkom Purwokerto. (Unpublished)

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Abstract

In this modern era, technology is increasingly developing by leaps and bounds, so the face is used as a part that can be recognized by computers. Face detection or facial recognition in a photo is a technique used to carry out the facial recognition process on a computer. On a campus, there are those who do attendance using barcodes which are still considered ineffective because they require using 2 devices if the lecturer does not display the barcode or is only sent through online media. Therefore, in this study, he chose to do attendance by detecting student identity card (KTM) objects using the CNN (Convolutional Neural Network) method in the form of an application based with the name "Ruang Absen". The purpose of making the project is to make it easier for students to do attendance during lectures, because they only need a KTM that all students must have. In the use of the CNN architecture, the accuracy value is 0.99, the precision is 0.99 and the recall is 0.99. Next, a test and identification analysis is carried out to obtain the percentage of accuracy of the system as a whole. Then in identifying photos on the KTM can be done with different or random positions used for the attendance system.

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