DESIGN OF MICROSLEEP DETECTION SYSTEM IN 32-BIT MICROCONTROLLER-BASED MOTORISTS WITH RANDOM FOREST METHOD

Syiva Awaliyah Maqdis, Anugrah Adiwilaga, Munawir Munawir

Abstract


The number of motorcycle accidents has increased rapidly every year. Many occur due to drowsiness or fatigue because motorists force themselves to keep driving. The state of fatigue while driving is also known as microsleep. To overcome this problem, we propose a design of a prototype system that can be installed on the helmet of a motorized user so that the driver is more alert when driving a vehicle. This system utilizes machine learning technology with the Random Forest algorithm with two prediction results: prediction 1, which means the motorcyclist is tired, or prediction 0, which means the motorcyclist is in a normal state, embedded in the ESP32 microcontroller, and a tilt sensor that can detect signs of drowsiness in motorists. This system design will use the MPU6050 sensor to measure changes in the angle of the motorcyclist's head. The microcontroller will process the data obtained to identify head changes that indicate the possibility of drowsiness. If it occurs, the buzzer will beep as a warning to warn the driver to take a short break. The test results in drowsiness conditions with an angle of 10°–30° resulted in 100% accuracy, and normal conditions only at an angle of 0°–6° resulted in 100% accuracy. The result of the developed system is expected to reduce the number of accidents caused by drowsiness

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References


S. A. Khan, H. Mukhatar, and B. A. Pramudita, “Perancangan Sistem Pendeteksi Microsleep Untuk Peringatan Kelelahan Pada Pengemudi Kendaraan,” EProceedings Eng., vol. 9, no. 4, p. 1810, Aug. 2022.

A. Hertig-Godeschalk, J. Skorucak, A. Malafeev, P. Achermann, J. Mathis, and D. R. Schreier, “Microsleep episodes in the borderland between wakefulness and sleep,” Sleep, p. zsz163, Jul. 2019, doi: 10.1093/sleep/zsz163.

Andre Hartoko Aji Putra Perdana, Susijanto Tri Rasmana, and Heri Pratikno, “Implementasi Sistem Deteksi Mata Kantuk Berdasarkan Facial Landmarks Detection Menggunakan Metode Regression Trees,” J. Technol. Inform. JoTI, vol. 1, no. 1, pp. 1–9, Oct. 2019, doi: 10.37802/joti.v1i1.1.

S. Sugeng and T. N. Nizar, “Deteksi Aktivitas Mata, Mulut Dan Kemiringan Kepala Sebagai Fitur Untuk Deteksi Kantuk Pada Pengendara Mobil,” Komputika J. Sist. Komput., vol. 12, no. 1, pp. 83–91, May 2023, doi: 10.34010/komputika.v12i1.9688.

N. W. Yanto, L. Elisana, S. W. Lestari, and W. Handini, “Rancang Bangun Sistem Pendeteksi Kantuk Menggunakan Sensor Imu dan WeMOS,” J. Teknol., vol. 9, no. 1, pp. 72–79, Nov. 2021, doi: 10.31479/jtek.v9i1.136.

C. Aj. Saputra, D. Erwanto, and P. N. Rahayu, “Deteksi Kantuk Pengendara Roda Empat Menggunakan Haar Cascade Classifier Dan Convolutional Neural Network,” JEECOM J. Electr. Eng. Comput., vol. 3, no. 1, pp. 1–7, Apr. 2021, doi: 10.33650/jeecom.v3i1.1510.

D. N. Aditya, C. Nugraha, and H. Prassetiyo, “Perancangan Alat Deteksi Dini Kondisi Kantuk untuk Mengurangi Risiko Kecelakaan Kerja Berbasis Pengolahan Citra Digital.pdf.” e-Proceeding FTI, 2022.

F. Damarjati, “Model Mitigasi Kecelakaan Transportasi Menggunakan Pencegah Micro-Sleep,” vol. 2, 2022.

B. I. S. Duna, Y. Iashania, and I. Tambang, “Manajemen Fatigue Untuk Mencegah Microsleep Pada Driver Sarana,” vol. 23, 2023.

R. P. Firdaus Ariandika Nadapdap, J. Muhamad Jabbar, and M. Pamungkas, “Faktor Risiko Microsleep Pada Driver Ojek Online di Antapani Tahun 2023,” STIKES Dharma Husada, Bandung, 2023.

H. Z. Ilmadina, D. Apriliani, and D. S. Wibowo, “Deteksi Pengendara Mengantuk dengan Kombinasi Haar Cascade Classifier dan Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 7, no. 1, pp. 1–7, Jan. 2022, doi: 10.30591/jpit.v7i1.3346.

A. Suprayogi and H. Fitriyah, “Sistem Pendeteksi Kecelakaan Pada Sepeda Motor Berdasarkan Kemiringan Menggunakan Sensor Gyroscope Berbasis Arduino,” J. Pengemb. Teknol. Įnformasį Dan Įlmu Komput., vol. 3, no. 3, pp. 3079–3085, Mar. 2019.

G. G. Putra and D. U. Suwarno, “Pembaca Aktivitas Manusia Dengan Sensor Gyro,” vol. 1, no. 1, 2019.

S. Edy and A. Hamzah, “Rancang Bangun Prototype Cruise Control Pada Kendaraan Listrik Dengan Metode Kendali PID,” ISTN, Reseacrh Report, 2023.

M. T. D. Putra, A. Adiwilaga, A. Clarissa, A. A. P. Gustiansyah, A. D. Kurniadi, and Z. Mumtaz, “Alat Bantu Tuna Netra Berbasis Arduino Uno dan Artificial Intelligence dengan metode YOLO v7,” J. Ilm. FIFO, vol. 15, no. 2, p. 159, May 2024, doi: 10.22441/fifo.2023.v15i2.007.

F. A. Rafrastaraa, R. A. Pramunendar, D. P. Prabowo, E. Kartikadarma, and U. Sudibyo, “Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 217–223, Jul. 2023, doi: 10.47233/jteksis.v5i3.854.

I. L. Mulyahati, “Implementasi Machine Learning Prediksi Harga Sewa Apartemen Menggunakan Algoritma Random Forest Melalui Framework Website Flask Python (Studi Kasus: Apartemen di DKI Jakarta Pada Website mamikos.com )”.

M. Munawir, D. R. Agustini, R. Rahmawati, and A. M. N. Anzhar, “Machine Diagnosis based Multi-Agent Technology by Autonomous Sensor with Energy Harvesting,” J. Comput. Eng. Electron. Inf. Technol., vol. 1, no. 1, pp. 51–62, Apr. 2022, doi: 10.17509/coelite.v1i1.43818.

F. Mangkusasmito, D. Y. Tadeus, H. Winarno, and E. Winarno, “Peningkatan Akurasi Sensor GY-521 MPU-6050 dengan Metode Koreksi Faktor Drift,” Ultima Comput. J. Sist. Komput., vol. 12, no. 2, pp. 91–95, Nov. 2020, doi: 10.31937/sk.v12i2.1791.

A. N. Syafia, M. F. Hidayattullah, and W. Suteddy, “Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS,” J. Inform. J. Pengemb. IT, vol. 8, no. 3, pp. 207–212, Sep. 2023, doi: 10.30591/jpit.v8i3.5064.

Universitas Singaperbangsa Karawang and S. Syihabuddin Azmil Umri, “Analisis dan Komparasi Algoritma Klasifikasi Dalam Indeks Pencemaran Udara di DKI Jakarta,” JIKO J. Inform. Dan Komput., vol. 4, no. 2, pp. 98–104, Aug. 2021, doi: 10.33387/jiko.v4i2.2871.

G. A. Sandag, “Prediksi Rating Aplikasi App Store Menggunakan Algoritma Random Forest,” CogITo Smart J., vol. 6, no. 2, pp. 167–178, Dec. 2020, doi: 10.31154/cogito.v6i2.270.167-178.

M. Amirullah and H. Kusuma, “Sistem Peringatan Dini Menggunakan Deteksi Kemiringan Kepala pada Pengemudi Kendaraan Bermotor yang Mengantuk,” vol. 7, no. 2, 2018.




DOI: https://doi.org/10.33387/jiko.v7i2.8539

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