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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters

This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. The purpose of this study is to evaluate and compare the performance of these algor...

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Main Authors:	Teguh, Sutanto, Muhammad Rafli, Aditya, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General)
Online Access:	http://eprints.intimal.edu.my/2047/1/jods2024_48.pdf http://eprints.intimal.edu.my/2047/2/588 http://eprints.intimal.edu.my/2047/ http://ipublishing.intimal.edu.my/jods.html
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id	my-inti-eprints.2047
record_format	eprints
spelling	my-inti-eprints.20472024-11-26T06:15:07Z http://eprints.intimal.edu.my/2047/ Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters Teguh, Sutanto Muhammad Rafli, Aditya Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. The purpose of this study is to evaluate and compare the performance of these algorithms in terms of accuracy. The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. The results showed that the application of the Stacking method with Gradient Boosting Meta-learner produced the highest accuracy of 96.00%, outperforming all other algorithms. In comparison, Random Forest achieved 95.75% accuracy, followed by SVM with 93.25% accuracy, and Logistic Regression and KNN each achieved 90.19% accuracy. This finding emphasizes that Stacking with Gradient Boosting provides much better performance in water quality classification compared to other models. This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2047/1/jods2024_48.pdf text en cc_by_4 http://eprints.intimal.edu.my/2047/2/588 Teguh, Sutanto and Muhammad Rafli, Aditya and Haldi, Budiman and M.Rezqy, Noor Ridha and Usman, Syapotro and Noor, Azijah (2024) Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters. Journal of Data Science, 2024 (48). pp. 1-7. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Teguh, Sutanto Muhammad Rafli, Aditya Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
description	This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. The purpose of this study is to evaluate and compare the performance of these algorithms in terms of accuracy. The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. The results showed that the application of the Stacking method with Gradient Boosting Meta-learner produced the highest accuracy of 96.00%, outperforming all other algorithms. In comparison, Random Forest achieved 95.75% accuracy, followed by SVM with 93.25% accuracy, and Logistic Regression and KNN each achieved 90.19% accuracy. This finding emphasizes that Stacking with Gradient Boosting provides much better performance in water quality classification compared to other models. This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.
format	Article
author	Teguh, Sutanto Muhammad Rafli, Aditya Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah
author_facet	Teguh, Sutanto Muhammad Rafli, Aditya Haldi, Budiman M.Rezqy, Noor Ridha Usman, Syapotro Noor, Azijah
author_sort	Teguh, Sutanto
title	Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
title_short	Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
title_full	Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
title_fullStr	Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
title_full_unstemmed	Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
title_sort	comparison of logistic regression, random forest, svm, knn algorithm for water quality classification based on contaminant parameters
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2047/1/jods2024_48.pdf http://eprints.intimal.edu.my/2047/2/588 http://eprints.intimal.edu.my/2047/ http://ipublishing.intimal.edu.my/jods.html
_version_	1817849525446901760
score	13.223943

Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters

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