The critical land is marked by the destruction of soil structure, decreasing the quality and quantity of organic matter, nutrient deficiency and disruption of the hydrological cycle, need to be rehabilitated and increased productivity so that the land can return to function as a good ecosystem or produce something that is economic to humans. Critical land causes: Forest encroachment, illegal logging, forest fires, utilization of non-sustainable forest resources, zoning of the area not yet in place, land management pattern is not conservative, diversion of land status (various interests). Data mining technology is one way to get information hidden from the data set to become knowledge that can support an organization in making decisions. After the number of clusters is known, a new cluster process is performed without following the hierarchy process. The formulation of the problem solved in this research is how to determine the rehabilitated priority based on land criticality level using K-means Alogitma. The K-Means algorithm is simple to implement and run, relatively fast, adaptable and common in practice.
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