Abstract
Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.
Highlights
Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students
This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi
Dari penelitian yang dilakukan didapatkan hasil terbentuknya sistem presensi terintegrasi web, mobile, dan service yang menerapkan arsitektur deep learning berbasis CNN yaitu FaceNet. FaceNet digunakan sebagai feature extractor yang dikombinasikan dengan SVM sebagai classifier
Summary
Pada Gambar 4 diperlihatkan ilustrasi topologi sistem presensi berbasis deep learning. Sistem ini diterapkan di dalam ruangan yang memiliki sebuah IP CCTV, sebuah mini komputer dan sebuah wireless access point yang. Spesifikasi Hardware untuk Training terhubung dengan jaringan kampus. Spesifikasi 2,4 GHz Intel Core i7 8 GB 1600 MHz DDR3 inilah script Python yang menerapkan model deep learning ditempatkan. Mini komputer terkoneksi dengan IP CCTV dan jaringan kampus.
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