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All Journal The Indonesian Journal of Computer Science
Fanny Olivia
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Predictive Analytics for Water Safety: Data Mining and Supervised Learning in Potability Classification Nanda Aulia Sofiah; Fanny Olivia; Jambak, Muhammad Ihsan
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3884

Abstract

Water is crucial for survival, especially for consumption, yet its quality is under threat due to human-caused pollution. Contaminated water poses serious health risks, including the transfer of diseases transmitted by water. Therefore, assessing water quality is critical for ensuring its safety for consumption. Data mining and supervised machine learning algorithms can help classify water potability, revealing hidden patterns and correlations between water parameters. This study evaluates the effectiveness of K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Neural Network methods in categorizing a water quality dataset. The evaluation is aimed at selecting the most accurate procedure, as indicated by the highest accuracy rate. Results show that Neural Network exceeds KNN (81%), Naïve Bayes (63%), and SVM (73%), with a 85% accuracy rate. Keywords : Classification, Data Mining, Supervised Machine Learning, Water Potability
Co-Authors Jambak, Muhammad Ihsan Nanda Aulia Sofiah