Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Applied Geospatial Information
Angga Dwinovantyo
Research Center for Oceanography, The National Research and Innovation Agency (BRIN). Jl. Pasir Putih Raya No.1, Jakarta 14430, Indonesia
Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering Environmental Science Physics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data Steven Solikin; Angga Dwinovantyo; Henry Munandar Manik; Sri Pujiyati; Susilohadi Susilohadi
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6363

Abstract

Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093.
Co-Authors Henry Munandar Manik Sri Pujiyati Steven Solikin Susilohadi Susilohadi