<![CDATA[A company needs to consider determining the customers’ potential before deciding to upgrade their bandwidth. It is important because, previously, determination was conducted randomly. Therefore, potential determination is necessary by grouping customers who have similar characteristics based on their data and attributes. This study employs data mining techniques using clustering method with K-means algorithm on broadband users’ group of 263 FTTH. The determination was determined based on end centroid point in the grouping. The results were divided into 5 clusters consisting of 34 highly potential users (12.92%), 29 potential users (11.02%), 56 fairly potential users (21.3%), 54 less potential users (20.53%), and the remaining 90 not potential users (34.22%). The comparison of the validity of the Davies-Bouldin Index for the 5 (five) clusters is 0.538 for K-Means and 0.819 for K-Medois. This indicates that K-Means results better score. This method is useful for efficient bandwidth sharing.]]>
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