Penentuan Tingkat Kesehatan Komunitas Mangrove Secara Otomatis Menggunakan Otsu Thresholding - Sarimuddin Sarimuddin; Mutmainnah Muchtar; Yuwanda Purnamasari Pasrun; Laode Abdul Fajar Hasidu; Riska Riska (Jurnal Informatika dan Rekayasa Perangkat Lunak, 2024)
Mangrove forests are one type of forest that grows in tropical and subtropical regions and plays a crucial role in maintaining ecosystem balance. One way to assess the health of mangrove communities is through regular monitoring and evaluation using modern technology such as digital image processing. The percentage of mangrove canopy cover can serve as a key indicator in evaluating the health and population density of mangrove communities. This study aims to implement the Otsu thresholding method in a digital image processing-based system capable of automatically determining the health status of mangrove communities based on the percentage of mangrove canopy cover. Mangrove canopy cover images were acquired using hemispherical photography techniques. Experimental results show that the system built using the Otsu thresholding method has an average Relative Absolute Error (RAE) of 0.034 and average Mean Error (ME) of 0.052, with an average processing time of 5.3 seconds. This indicates that the system can automatically determine the health status of mangrove communities in a relatively short time. It also suggests that the process of determining the health status of mangrove communities aligns with direct field observations.