<![CDATA[PT. PLN (Persero) UP3 Binjai faces challenges in handling electricity usage violations that increase every year. Lack of utilization of data violations that can be utilized to produce useful information in supporting strategic decision making by PLN, especially in the implementation of Electricity Usage Control (P2TL) activities. This study aims to identify customer violation patterns based on rayon, power, and type of violation with data mining methods using the K-Means Clustering algorithm. The results of the study show that the 3rd cluster represents the most violation-prone area, namely in the West Binjai Rayon, with a power of 450 VA and the most frequent type of P2 violation. The results of the study show that the K-Means algorithm with the Elbow method is able to systematically group data violations based on certain characteristics. The results of this study can provide recommendations to PLN UP3 Binjai to improve the effectiveness of monitoring and enforcement strategies through a more targeted approach. ]]>
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