Analysis of Clean Water Consumption Segmentation And Classification Using K-Means Clustering And Random Forest Algorithms - Ika Melinia Sapitri Fitriyanti; Sarjo Defit; Rini Sovia (Universitas Putra Indonesia YPTK Padang, 2026)
The administrative grouping of PERUMDA Air Minum Kota Padang customers is not yet able to accurately represent actual customer water consumption patterns. This condition makes it difficult for the company to formulate service policies, customer management, and make appropriate data-based decisions. This study aims to analyze and map customer water consumption patterns to produce more representative customer segmentation as a basis for decision making. The research method used is a data mining approach with the application of Principal Component Analysis (PCA) for dimension reduction, K-Means Clustering for customer segmentation, and Random Forest for customer classification, using primary data from the Padang City Water Company's Customer Meter Reading Report with an initial amount of 371 data. The results of the study show that the clustering process successfully formed three customer segments, namely premium customers with high consumption bills, regular customers with moderate and stable consumption, and new customers with low consumption rates. The evaluation of the Random Forest model's performance resulted in an accuracy rate of 68.85% on the training data and 67.69% on the testing data, with an average precision value above 0.84 and an average F1-score value of around 0.68. The consistency of performance between the training data and the testing data shows that the model has fairly good generalization capabilities and does not experience overfitting.