Transformasi Data Multi-Sensor dan Kompresi Data Gambar Air Pasang pada Banjir Rob Berbasis Internet of Things Menggunakan Algoritma Discrete Cosine Transform

Abstract

Coastal flooding (rob flood) is a frequent disaster in coastal areas caused by high tidal waves and strong winds, impacting infrastructure and communities. To enhance monitoring and mitigation, this study develops a multi-sensor data transformation and compression system based on the Internet of Things (IoT) using the Discrete Cosine Transform (DCT) algorithm. The system utilizes an anemometer and wind vane sensor to measure wind speed and direction in real time. The sensor data undergoes transformation, where the wind vane converts raw numerical codes (*1# to *8#) into wind direction information, while the anemometer converts pulse into wind speed in km/h. Additionally, monitoring images are compressed using DCT and downsampling, achieving a compression ratio of 116.29, reducing the original file size from 694,137 bytes to 5,969 bytes without losing essential information. The results indicate that this system improves monitoring efficiency through accurate sensor data transformation and optimal image compression, supporting faster and more effective decision-making in rob flood mitigation.