<![CDATA[PT Semen Indonesia, an industrial company based in Gresik, East Java, is committed to enhancing operational efficiency and managing maintenance costs effectively. By analyzing patterns in maintenance frequency, total costs, and maintenance duration across their various plants, the company can identify work units that require more intensive attention or that can be optimized for greater efficiency. To achieve this, PT Semen Indonesia employs K-Means clustering analysis to gain deeper insights into the maintenance data, identifying patterns that can help improve operational efficiency and develop more targeted maintenance strategies based on the identified clusters. The clustering of planner groups is carried out using variables such as the number of maintenance activities, total costs, and duration of maintenance tasks. As a result of the K-Means clustering, the planner groups have been divided into two clusters: Cluster 1, which consists of planner groups that perform more efficiently, and Cluster 2, which includes those with less efficient performance. Based on these clustering results, PT Semen Indonesia should conduct further evaluation or review of the planner groups in Cluster 2.]]>
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