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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2047/ http://eprints.intimal.edu.my/2047/1/jods2024_48.pdf http://eprints.intimal.edu.my/2047/2/588 |
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| 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 |
| building | INTI Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:58:35Z |
| format | Article |
| id | intimal-2047 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:35Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/2047/ http://eprints.intimal.edu.my/2047/ http://eprints.intimal.edu.my/2047/1/jods2024_48.pdf http://eprints.intimal.edu.my/2047/2/588 |