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
Article Metrics:
Last update:
Upon acceptance for publication, authors agree to transfer the copyright of their article to Jurnal Kelautan Tropis, while retaining the right to reuse their work under the terms of the open license applied.
From the date of publication, the copyright for each article is held by Jurnal Kelautan Tropis. This transfer allows the journal to manage, disseminate, and preserve scholarly content in accordance with international standards and open access best practices.
Although copyright is held by the journal, all published articles are made available under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). Under this license, anyone may:
Copy and redistribute the material in any medium or format
Remix, transform, and build upon the material for any purpose, even commercially
provided that:
Appropriate credit is given to the original author(s) and the source
Indications are made of any changes that were made
Derivative works are distributed under the same license (CC BY-SA 4.0)
While copyright is held by the journal, authors retain important reuse rights. Authors may:
Reuse the published version of their article in future works, including books, compilations, and lectures
Deposit the published version in institutional or subject repositories
Share the article freely, including on personal websites or academic networks
as long as the original publication in Jurnal Kelautan Tropis is cited and the CC BY-SA 4.0 license terms are respected.
Authors must ensure that any third-party content included in the article (e.g., figures, images, datasets) is either original, in the public domain, or licensed for reuse under compatible terms. If specific permissions are required, authors must obtain them prior to submission.
For questions regarding copyright or licensing, please contact the editorial office at: j.kelautantropis@gmail.com
View My Stats
Jurnal Kelautan Tropis is published by Departement of Marine Science, Faculty of Fisheries and Marine Science, Universitas Diponegoro under a Creative Commons Attribution-ShareAlike 4.0 International License.