Design And Construction of Iot-Based Accident-Prone Area Detector Using Data Logging Method At Dau District Police Sector Area

This research develops an Internet of Things (IoT)-based accident-prone area detection tool by utilizing an ESP32 microcontroller, gyroscope sensor, LDR sensor, and Neo Ublox GPS connected via a Mi-Fi network. The focus of this research is on the high number of traffic accidents in high-risk areas due to the lack of warning signs and the lack of real-time road condition information. The developed tool collects numerical data from various sensors to detect road conditions that have the potential to trigger accidents, such as road slope and light intensity. The gyroscope sensor, after being calibrated, showed an average error of 0.02 degrees on the X-axis and 0.01 degrees on the Y-axis, with a maximum error of 0.03 degrees in testing, ensuring accurate detection of road slope, which is very important for identifying accident-prone areas. The Neo Ublox GPS sensor showed high accuracy in location tracking, with the difference between real-time GPS coordinates and reference traffic data usually below 0.05 meters, except for one instance when a deviation of 27.59 meters was recorded. This high level of accuracy is essential to provide reliable geofencing and location-based alerts. The data is systematically processed using data logging methods, allowing analysis to find patterns and relationships between road conditions and accident risk. As part of this system, an Android application was also developed to display the data visually, with increased accuracy through the use of geofencing technology and provide notifications when entering a geofenced area. The study findings show that the tool is capable of providing real-time alerts to road users, thereby increasing their awareness of potential hazards in accident-prone locations.