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Alat Monitoring Kecelakaan Dengan Intelligent Transport System Berbasis Internet of Things Junio Andika Danda; Ade Silvia Handayani; Sopian Soim; Nyayu Latifah Husni; Leni Novianti
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4652

Abstract

Accidents are one of the problems that are always faced in big cities. This is evident from the indications that traffic accident numbers are always increasing. Delays in handling accidents often occur due to delays in information received by the police and nearby hospitals. Therefore, a remote monitoring system is needed to control vehicles that are operating in real time using monitoring tools that are able to detect traffic accidents and emergency events on the way In this study, the Internet of Things-based Intelligent Transport System was applied using the Support Vector Machine method whose design was carried out using accelerometer sensors, vibrating sensors and sound sensors with the addition of NEO 6M GPS as a coordinate point information provider and a PI NoIR Camera to capture images of surrounding conditions when an accident occurred. The way this tool works is that when there is an accident in a vehicle (car) this tool has been installed with various sensors and components that are able to analyze the surrounding situation if this car has an accident, it will send data that will be displayed in the Android application in the form of time data, coordinates, vibration, sound and images.
Perancangan Aplikasi Android pada Alat Monitoring Kecelakaan dengan Intellegent Transport System Muhammad Dandy Pratama Putra; Ade Silvia Handayani; Ing. Ahmad Taqwa; Nyayu Latifah Husni; Leni Novianti
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2059

Abstract

The increasing number of transportation also increases the number of traffic accidents. It can happen not only in the open road but also in the quiet road so that the bad things can happen to the accident victims. The delays of handling the accidents often occur due to the delay information that received by the police or the nearest hospital. This is due to the information that obtained by local people and forwarded to the authorities is slow. Therefore a system that can monitor the vehicle remotely will be needed nowdays. By building an accident monitoring system using the Intelligent Transport System (ITS), it is hoped that it can monitor the vehicle and through Android can directly check the coordinates of the vehicle and the condition of the vehicle. In the design of the accident monitoring system tool using the SVM (Support Vector Machine) method. Testing this tool uses hardware consisting of Rasberry PI 3, Arduino Uno, Sensor FC04, Accelerometer 6050, Vibration Shock Sensor, Camera Pi Noir, panic button, GPS and Android Application as an interface with the user. The results of this research show that the tool made has a percentage of delays between 1 to 5 seconds, all data from the accident monitoring system tool is directly sent to the Android application in real time based on the internet network speed connection
Implementasi Support Vector Machine Pada Alat Monitoring Kecelakaan Dengan Intelligent Transport System Syifa Amira Zahrah; Ade Silvia Handayani; Ali Nurdin
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1974

Abstract

The implementation of intelligent transportation systems will produce a large amount of data. The resulting data is critical in the design and implementation of ITS in the transportation system. This study discusses the performance of the Support Vector Machine algorithm on an accident monitoring tool by utilizing the Intelligent Transportation System that works in real-time using an Android-based application. This experiment simulates accident monitoring with a multisensor accident monitoring device. Multisensor technology consists of MPU 6050 sensor, sound sensor, vibration sensor, and camera. In an experiment, the measured variables are location, slope, accuracy, and time of the traffic accident monitoring system. The results of monitoring traffic accidents in testing using the Support Vector Machine algorithm can work well by classifying data based on the type of accident.