<![CDATA[Determining road construction priorities at the Garut Regency Public Works and Spatial Planning (PUPR) Office could be more effective in terms of validity in achieving goals and recap time because it needs good clustering. This study uses the Cross-Industry Standard Process for Data Mining and the k-means algorithm to classify road construction data with type, length, and construction cost attributes. The results of this study are the selection of five clusters with the smallest Davies-Bouldin index evaluation value of 0.1617. The analysis of the characteristics of the largest cluster road construction is from Group 1, Group 2, Group 3, Group 5, and Group 4, with nominal development depending on the type of construction. From the clustering knowledge and cluster characteristics results, the Garut District PUPR office can prioritize the construction of small roads more than the construction of medium or large roads by looking at the submissions based on the cluster.]]>
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