Web-Based Extreme Weather Monitoring and Early Warning System for Traditional Fishermen Using IoT Sensor Data

Abstract

Extreme weather can disrupt traditional fishing activities and increase safety risks, particularly when local weather information is difficult to access before departure. This study develops a web-based extreme weather monitoring and early warning system for traditional fishermen using Internet of Things (IoT) sensor data. The system integrates an ESP32 microcontroller with a DHT22 sensor, rain sensor, and anemometer to acquire temperature, humidity, rainfall, and wind-speed data at ten-second intervals. Sensor data are processed using thresholds combined with a scoring scheme, transmitted through HTTP to a PHP server, stored in MySQL, and visualized through a web dashboard. The system also provides Telegram Bot notifications at 06:00 WIB and 17:00 WIB and event-triggered warnings when the classified condition reaches the danger category. Functional testing using black-box testing produced successful results for the tested features, while Perfetto UI measurements showed response times between 0.56 and 1.34 seconds, with an average of 0.95 seconds. The implementation demonstrates that a low-cost, locally deployed IoT monitoring system can combine real-time weather observation, simple risk classification, web visualization, and automatic notification to support weather awareness before traditional fishermen depart.