1Program Studi Magister Teknologi Kelautan, Fakultas Perikanan dan Ilmu Kelautan, Institut Pertanian Bogor, Indonesia
2Departemen Ilmu dan Teknologi Kelautan, Fakultas Perikanan dan Ilmu Kelautan, Institut Pertanian Bogor, Indonesia
BibTex Citation Data :
@article{JKT26778, author = {Mochamad Rafif Rabbani and Henry Munandar Manik and Totok Hestirianoto}, title = {Klasifikasi Gelembung Gas Menggunakan Multibeam Echosounder dan Machine Learning}, journal = {Jurnal Kelautan Tropis}, volume = {28}, number = {2}, year = {2025}, keywords = {Gelembung Gas; Multibeam Echosounder; Machine Learning; Matrix Confusion; Karangantu}, abstract = { 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. Kepentingan pendeteksian gelembung gas di kolom air menjadi urgensi dalam berbagai bidang, misalnya dalam pemantauan lingkungan hingga deteksi kebocoran gas bawah laut. Salah satu metode yang dapat digunakan dalam mendeteksi gelembung gas adalah dengan menggunakan Multibeam Echosounder . Namun, pengolahan data Multibeam Echosounder rawan terjadi human error dan tidak cukup efisien dalam skala waktu, sehingga diperlukan metode praktis dalam pengolahan data Multibeam , salah satunya adalah dengan menggunakan bantuan Artificial Intelligence , yaitu Machine Learning . Penelitian ini bertujuan untuk mengklasifikasikan gelembung gas dengan menggunakan Multibeam Echosounder dan Machine Learning, serta menentukan algoritma terbaik. Data akustik yang telah diakusisi diolah terlebih dahulu dengan bantuan perangkat lunak FMMidwater Fledermaus untuk ekstraksi fitur dan kedalaman objek di kolom air, serta proses tagging target pada echogram sebagai proses pelabelan secara visual untuk input pada model Machine Learning . Terdapat tiga algoritma yang diuji, yaitu Random Forest, K-Nearest Neighbor dan Support Vector Machine . Evaluasi model menggunakan confusion matrix untuk menghasilkan nilai akurasi, F1-Score dan koefisien kappa. Evaluasi performa model menunjukkan algoritma Random Forest memiliki nilai akurasi tertinggi yaitu 89.02 % diikuti oleh Support Vector Machine dengan akurasi 86.76% dan K-Nearest Neighbor dengan akurasi 85.41%. Hasil ini membuktikan bahwa pendekatan Machine Learning mampu mengklasifikasikan gelembung gas di kolom air serta dapat membedakannya terhadap objek lain di kolom air }, issn = {2528-3111}, pages = {247--254} doi = {10.14710/jkt.v28i2.26778}, url = {https://ejournal2.undip.ac.id/index.php/jkt/article/view/26778} }
Refworks Citation Data :
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.
Kepentingan pendeteksian gelembung gas di kolom air menjadi urgensi dalam berbagai bidang, misalnya dalam pemantauan lingkungan hingga deteksi kebocoran gas bawah laut. Salah satu metode yang dapat digunakan dalam mendeteksi gelembung gas adalah dengan menggunakan Multibeam Echosounder. Namun, pengolahan data Multibeam Echosounder rawan terjadi human error dan tidak cukup efisien dalam skala waktu, sehingga diperlukan metode praktis dalam pengolahan data Multibeam, salah satunya adalah dengan menggunakan bantuan Artificial Intelligence, yaitu Machine Learning. Penelitian ini bertujuan untuk mengklasifikasikan gelembung gas dengan menggunakan Multibeam Echosounder dan Machine Learning, serta menentukan algoritma terbaik. Data akustik yang telah diakusisi diolah terlebih dahulu dengan bantuan perangkat lunak FMMidwater Fledermaus untuk ekstraksi fitur dan kedalaman objek di kolom air, serta proses tagging target pada echogram sebagai proses pelabelan secara visual untuk input pada model Machine Learning. Terdapat tiga algoritma yang diuji, yaitu Random Forest, K-Nearest Neighbor dan Support Vector Machine. Evaluasi model menggunakan confusion matrix untuk menghasilkan nilai akurasi, F1-Score dan koefisien kappa. Evaluasi performa model menunjukkan algoritma Random Forest memiliki nilai akurasi tertinggi yaitu 89.02 % diikuti oleh Support Vector Machine dengan akurasi 86.76% dan K-Nearest Neighbor dengan akurasi 85.41%. Hasil ini membuktikan bahwa pendekatan Machine Learning mampu mengklasifikasikan gelembung gas di kolom air serta dapat membedakannya terhadap objek lain di kolom air
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