Deteksi Kantuk Pengemudi Berbasis Eye Aspect Ratio dan Head Pose Estimation dengan Integrasi IoT - Rama Amirul Ramadhan; Kurniawan D. Irianto (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
This study proposes a real-time driver drowsiness detection system integrating computer vision with Internet of Things (IoT) technology on an affordable embedded platform. The system uses a Camera Module 3 Wide NoIR connected to a Raspberry Pi 3 Model B+ to capture driver facial images. Two visual indicators are computed in parallel from 68 facial landmarks extracted using dlib: Eye Aspect Ratio (EAR) for detecting prolonged eye closure, and Head Pose Estimation using solvePnP for detecting head nodding. An OR-logic decision mechanism triggers an audio alarm when EAR falls below 0.25 for five consecutive frames or Pitch angle exceeds 15 degrees for ten consecutive frames. Events are classified as KANTUK_MATA, KANTUK_KEPALA, or KEDUANYA and sent to firebase Realtime Database for remote monitoring. Black Box testing with 11 scenarios confirms all core functions operate correctly. Average response times of 2.00 seconds via EAR and 2.70 seconds via Head Pose are within acceptable ranges for early drowsiness warning. The multi-indicator approach demonstrates that head nodding is detected earlier than eye closure in gradual drowsiness scenarios, providing earlier warning than single-indicator systems. Under high CPU load conditions (>80%), the frame rate drops to 5 fps, resulting in a total system latency of 2.8–4.2 seconds; this condition is still adequate for early warning but not for sub-second responses. Across the 11 Black Box testing scenarios, the system achieved 100% precision (no false alarms during normal blinking) and 100% recall (all drowsiness conditions detected), although the testing was conducted under controlled laboratory conditions, necessitating further generalization to real-world environments.