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All Journal International Journal of Applied Sciences and Smart Technologies
Hendra Saputra, Putu
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Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Comparison of SVM, K-NN, RF, CART, and GNB Algorithms for Water Bodies Detection Using Sentinel-2 Level-2a Imagery in Nakhon Pathom, Thailand Nita Nathalia, Ni Putu; Andra Rizqy Wijaya, Gede; Yota Ernanda Aryanto, Kadek; Novita Puspa Dewi, Ni Putu; Hendra Saputra, Putu; Karisma Dewi, Ni Putu; Damayanti, Mellisa; Losinanda Prawira, Kadek
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 1, June 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i1.11608

Abstract

Satellite imagery is utilized in various fields, one of which is land use and land cover (LULC) analysis. This study aims to classify water bodies using machine learning models such as SVM, K-NN, RF, CART, and GNB. The data source is obtained from the Google Earth Engine (GEE) platform using Sentinel-2 Level-2A satellite imagery, with a dataset of 5,514 data points per year. The Pixel-Based approach is used as the main method for data extraction, while CRISP-DM is applied as a structured methodology for data management. The parameter indices used include the BSI, NDBI, MNDWI, NDVI and AWEIsh. The results of these calculations serve as dataset features for training algorithms in the model development and training process. Each model has its own parameters, making parameter selection crucial in the training process. Model evaluation is conducted using a confusion matrix. Based on confusion matrix analysis, accuracy, precision, recall, and F1-score are calculated. Among the five models, SVM achieves the highest accuracy at 87%, followed by RF and K-NN. This indicates that the SVM model performs better in binary classification. Ground truth analysis is also conducted using the QGIS platform, which visualizes the classification results, with SVM providing the best visualization.
Co-Authors Andra Rizqy Wijaya, Gede Damayanti, Mellisa Karisma Dewi, Ni Putu Losinanda Prawira, Kadek Nita Nathalia, Ni Putu Novita Puspa Dewi, Ni Putu Yota Ernanda Aryanto, Kadek