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All Journal Jurnal Elektronika dan Teknik Informatika Terapan
Endah Fitriani
UniversitasBina Darma
Computer Science & IT

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Pengembangan Sistem Presensi Cerdas Multimodal Berbasis Integrasi Fingerprint, ESP32-CAM, dan Time-of-Flight (ToF) Muthia Ananda; Endah Fitriani; Nina Paramytha IS; Leon A. Abdillah
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 4 No. 3 (2026): September: Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i3.1577

Abstract

Conventional student attendance at SD Negeri 182 Palembang requires manual classroom monitoring by teachers, which can consume instructional time and is vulnerable to recording errors. This study develops an autonomous attendance prototype that integrates an AS608 fingerprint sensor, ESP32-CAM face capture, and a VL53L0X Time-of-Flight (ToF) distance sensor, with Sugeno fuzzy logic as the decision mechanism and Google Sheets as the cloud-based attendance log. The system was designed using an ESP32-CAM as the main controller, C++ programming through Arduino IDE, an LCD 16×2 I2C as the local interface, and physical buttons for IN, OUT, and MENU operations. Testing was conducted on 28 fifth-grade students at SD Negeri 182 Palembang. The evaluation covered hardware operating voltage, fingerprint enrollment, ToF distance validation, end-to-end IN/OUT attendance simulation, and cloud data transmission. The enrollment test successfully registered all 28 students, giving a 100% enrollment success rate. The ToF gate successfully detected students at an ideal distance of up to 30 cm and rejected positions beyond the threshold. In attendance simulation, the average successful attendance rate was 64.29%, consisting of 75.00% for IN and 53.57% for OUT. The main failures were associated with fingerprint reading conditions, including wet, sweaty, dirty, dry, or shifted finger placement. Attendance records containing student ID, name, timestamp, IN/OUT status, and face-capture data were transmitted to Google Sheets through Google Apps Script. The study demonstrates that the proposed multi-sensor architecture can automate attendance recording and centralize attendance data, although biometric robustness and longer-duration testing remain necessary for further improvement.
Co-Authors Leon A. Abdillah Muthia Ananda Nina Paramytha IS