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: | , , , , , |
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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/2047/1/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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| Summary: | 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. |
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