Pengenalan Sinyal Otak Berbasis Machine Learning untuk Aktivasi Lampu Sen Otomatis pada Kendaraan Bermotor (Kasus Ibu-Ibu di Indonesia)

Hartantom, Budi
Marliana, Iin
Pramono, Doni Eko Hendro

Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
2025: SNESTIK V

This study proposes an automatic turn signal activation system for motor vehicles based on brain signals using a machine learning approach, with a specific focus on rider behavior, particularly among Indonesian mothers. The system is designed to enhance driving safety by detecting brain signals using EEG devices and processing them through machine learning algorithms to identify the rider's intent to activate the turn signals. Data were collected from various rider groups, processed, and trained using machine learning models to ensure high classification accuracy. The test results indicate that this system effectively recognizes brain signal patterns and automates turn signal activation with adequate accuracy. The implementation of this system is expected to reduce the risk of accidents caused by riders' negligence in providing signals when turning.