Klasifikasi Kondisi Kendaraan Berpotensi Kecelakaan Berbasis Android Menggunakan Long Short Term Memory - Nabila, Puspita Aliya; Soim, Sopian; Handayani, Ade Silvia (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2024)
Traffic accidents are a severe problem that often results in loss of life and property damage. Efforts to overcome this situation require real-time vehicle monitoring with the capture and collection of relevant data to provide information about the driver and family at home to reduce the risk of accidents by identifying potentially dangerous vehicle conditions automatically and quickly. This research utilizes Long Short Term Memory technology to analyze sensor data installed in the vehicle to an android device to be classified according to three conditions that recognize vehicle conditions as safe, alert, or dangerous. The Long Short Term Memory model used achieved a high level of accuracy with a value of 99.96% when training on data. After testing, this model still has a good level of accuracy with a value of 93.3%. In the test, the precision value of each class is 83.33% for the safe class, 80% for the danger class, and 100% for the alert class. In indicating that Long Short Term Memory in this study is very efficient in identifying and classifying vehicle conditions to reduce potential accidents. The information processed by Long Short Term Memory will be transmitted to an Android application capable of delivering up-to-date insights into the vehicle's condition. This app incorporates cautionary alerts in the presence of potential accident indicators to aid in vigilance and accident prevention. The integration of this system aims to enhance road safety and diminish the occurrence of accidents resulting from suboptimal vehicle conditions or hazardous driver conduct. This application can provide convenience for vehicle owners to know the state of the vehicle in real-time remotely in optimal conditions.