Protoype Monitoring Presensi Siswa Menggunakan Finger Print Dengan Kendali Raspberry Pi

Abstract

One character that can be shaped in students at school is discipline. The exact time to enter school is one of the criteria for discipline that is applied in the Aliyah Negri 2 Ponorogo Madarasah. In order to improve information services to student guardians, MAN 2 Ponorogo changed the way manual attendance was made to students with fingerprint technology. But there are shortcomings in the use of fingerprint machines, the presence data on the machine is not real time in reporting student attendance to schools and student guardians. The solution is to integrate a fingerprint machine with Raspberry pi control. The Raspberry pi will automatically process the presence data inside the fingerprint machine so that it can be accessed via the web wherever the user is located and automatically sends the attendance report to the guardian using the telegram media. The design of this study uses the waterfall method in designing application prototypes and the results are tested using the blackbox method with a boundary value analysis technique. In subjective testing the system will be tested using a questionnaire about the ease of using the system to users.

Pengembangan Aplikasi Mobile Presensi Mahasiswa Berbasis QR-Code Di Universitas Klabat

“…Rohman et al [6] melakukan penelitian dalam pembuatan sistem presensi fingerprint berbasis Android. Kemudian Setyawan et al [7] membuat prototype monitoring presensi siswa menggunakan fingerprint dengan kendali Raspberi PI. Septyanto et al [8], membuat aplikasi presensi pengenalan wajah dengan menggunakan algoritma Haar Cascade Classifier.…”

Integration Of Open CV LBF Model To Detect Masks In Health Protocol Surveillance Systems

“…Engineering that can be done is by modifying the face identification feature into a mask identification implanted in the device. (Cobantoro, 2018;Cobantoro et al, 2023;Masykur et al, 2020;Oktavianto et al, n.d.;Setyawan et al, 2020) Modification of facial identification features is done by adding face mark or landmark features. This feature is built using the LBF (Local Binary Features) model from Open CV (Computer Vision).…”