Klasifikasi Gelembung Gas Menggunakan Multibeam Echosounder dan Machine Learning - Mochamad Rafif Rabbani; Henry Munandar Manik; Totok Hestirianoto (Jurnal Kelautan Tropis, 2025)
The urgency of detecting gas bubbles in the water column is crucial in various fields, ranging from environmental monitoring to detecting underwater gas leaks. One method that can be used to detect gas bubbles is the Multibeam Echosounder. However, processing Multibeam Echosounder data is prone to human error and inefficient in terms of time, necessitating a more practical approach, such as utilizing Artificial Intelligence, specifically Machine Learning. This study aims to classify gas bubbles using Multibeam Echosounder and Machine Learning and determine the best algorithm. The acquired acoustic data were first processed using FMMidwater Fledermaus software for feature extraction and depth analysis in the water column, followed by target tagging on the echogram as a visual labeling process for Machine Learning model input. Three algorithms were tested: Random Forest, K-Nearest Neighbor, and Support Vector Machine. Model evaluation was conducted using a confusion matrix to generate accuracy, F1-score, and kappa coefficient values. The evaluation results showed that the Random Forest algorithm achieved the highest accuracy of 89.02%, followed by Support Vector Machine with 86.76% and K-Nearest Neighbor with 85.41%. These findings demonstrate that the Machine Learning approach effectively classifies gas bubbles in the water column and distinguishes them from other objects in the water column.