<![CDATA[The SAW (Simple Additive Weight) method is often also known as the weighted sum method. The basic concept of the SAW method is to find a weighted sum of the performance ratings for each alternative on all attributes (Fishburn, 2011), the SAW method requires the decision matrix normalization process (X) to a scale that can be compared with all existing alternative ratings. The SAW method is the most well-known and most widely used method in dealing with a Multiple Attribute Decision Making (MADM) situation. MADM itself is a method used to find optimal alternatives from a number of alternatives with certain criteria.Algortima K-Nearest Neighbor (K-NN) is a method for classifying objects based on learning data that is the closest distance to the object. K-Nearest Neighbor is based on the concept of 'learning by analogy'. Learning data is described by n-dimensional numeric attributes. Each learning data presents a point, marked with c, in n-dimensional space. Nearest Neighbor algorithm classification method that groups new data into several data / neighbors (neighbors) closest. So by combining the two algorithms will get the results in hiring employees at the company. In studies that have often been conducted on employee recruitment have been found, but in this study is to combine the SAW (Simple Additive Weightinh) method and the existing algorithm in Data Mining, the Nearest Neighbor algorithm. The final result in this study is to see the results of a combination of calculations SAW (Simple Additive Weight) with the Nearest Neighbor algorithm, the final results seen are the results of the work process in the calculation of the two methods. Keywords: Decision Support System; Simple Additive Weight (SAW); Nearest Neighbort]]>
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