Enhancing Water Quality Prediction using the Decision Tree Approach with the C4.5 Algorithm - Juniani, Anda Iviana; Nugraha, Anggara Trisna; Setiawan, Edy; Amelia, Putri; Handandi, Zukhruf Zidane; Hidayana, Elmi (JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science), 2025)
Water quality is essential for safeguarding both public health and the environment. This study aims to develop a predictive model for assessing water quality using the Dtree (Decision Tree) method with the C4.5 algorithm. The research involves analyzing water samples from different sites around Sidomulyo Village, focusing on key parameters such as pH, Total Dissolved Solids (TDS), and turbidity. The objective is to create a model that categorizes water samples according to class II water quality standards. The research process includes data collection, initial data preparation, model development with the C4.5 algorithm, and performance evaluation. The results reveal that the dtree model achieved a high accuracy rate of 95.65% for water quality prediction. The confusion matrix analysis demonstrated a precision of 92.31% for predicting class II standards and 100% for identifying samples that did not meet these standards. These findings underscore the effectiveness of the C4.5 algorithm in evaluating water quality. The model offers valuable insights for water resource managers and policymakers, aiding in improved water quality monitoring and management. This research makes a significant contribution to enhancing decision support systems for more effective water quality managementÂ