<![CDATA[The management of water billing in the PAMSIMAS service in Sidobandung Village is still conducted manually and does not provide early information regarding bill estimates, often resulting in delayed payments by customers. This study aims to design and develop a web-based water bill prediction application using the Multiple Linear Regression (MLR) method, capable of delivering fast, accurate, and accessible billing estimates. The dataset used in this research consists of historical monthly water usage and billing data from January to December 2024, with a structure comprising 231 rows of customer data and 30 feature columns. The research stages include data preprocessing, model training using MLR, integration of the model into a web-based system, and evaluation of prediction results using the Mean Squared Error (MSE) and R-squared ( ) metrics. Evaluation results showed that the model achieved an MSE of 18,882 and an of 0,8, indicating a fairly good and stable prediction performance. The system allows customers to log in, view predicted water bills for the 13th month based on previous data, and access graphical visualizations of usage and cost trends. Meanwhile, the admin can efficiently manage customer data through a dedicated dashboard. With the implementation of this application, the management and prediction process of water billing becomes more transparent, efficient, and helps customers in planning their water expenses more precisely .]]>
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