<![CDATA[Increasing customer bandwidth needs to be considered by companies by determining potential customers. Potential determination is needed because previously it was only done randomly. Therefore, first determining the potential is needed by grouping customers who have the same characteristics based on the data and attributes they have. This research will implement data mining technique with clustering method with K-means algorithm on 263 FTTH Broadband customer groups. Then the potency can be determined based on the final centroid point in the grouping. The results obtained are divided into 5 clusters consisting of 34 customers or 12.92% of the total potential customers, 29 customers or 11.02% of the total potential customers, 56 customers or 21.30% of the total potential customers, 54 customers. or 20.53% of the total fewer potential customers and 90 customers or 34.22% of the total non-potential customers. Comparison of the validity of the Davies-Bouldin Index cluster for 5 groups between K-Means and K-Medoids, the value of 0.538 for K-Means and 0.819 for K-Medoids is obtained. This method is used to streamline the distribution of bandwidth.]]>
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