Clustering State Electricity Company (PLN) Customer Electricity Consumption Patterns Using K-Means for Operational Efficiency - Anang Putranto; Imam Much Ibnu Subroto; Arief Marwanto (Bulletin of Computer Science Research, 2026)
Electricity consumption patterns among PLN customers show diverse characteristics that require data-driven analysis to support accurate operational planning and service strategies. Differences in installed power, operating hours, and electricity usage intensity can indicate variations in customer behavior, load demand, and service needs. This study aims to analyze electricity consumption patterns of PLN customers in the South Semarang area using the K-Means Clustering method. The dataset consists of 6,622 active customers, with variables including installed power, operating hours, and average electricity consumption over the last eight months. The research stages include identifying the need for consumption pattern analysis, preparing data, cleaning incomplete or inconsistent values, normalizing variables, modeling with K-Means, and interpreting cluster results. The optimal number of clusters was determined using the Elbow Method and Silhouette Score to ensure meaningful grouping and good separation quality. The results show that the optimal number of clusters is three. The first cluster represents low consumption patterns with 1,881 customers (28.4%). The second cluster represents medium consumption patterns with 2,927 customers (44.2%). The third cluster represents high consumption patterns with 1,814 customers (27.4%). The WCSS value of 498.67 and Silhouette Score of 0.68 indicate good clustering performance and practical usefulness for decision-making by PLN in service quality improvement.