Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes - Azmi, Fadhillah; Gibran, M Khalil; Ridwan, Achmad (AI Society & STMIK Indonesia, 2023)
In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.