<![CDATA[Bank X has branches all over indonesia, with operational process running for 24 hours everyday. Computer is certainly one of the most important technology for oprerational needs and banking activity. In order to keep the performance, Bank X has a routine of replacing their computer. This routine has become one of Bank X's biggest expenses. This is beause good quality and complex hardware and software require high cost, and the number of computers owned by Bank X. This replacement routine, has not been carried out effectively. From these problems, the research will be carried out related to the prediction of computer replacement using K-Nearest Neighbor Method. K-Nearest Neighbor method is a simple, fast, easy to understand, effective and accurate method for datasets with large data training. This research was conducted to determine the class of a computer, ‘Yes' or ‘No'. ‘Yes' indicated a computer needs to be replaced, and ‘No' indicates a computer does not need to be replaced. The Data Training used is in the form of 43713 computer data owned by Bank X throughout Indonesia. This method was tested with K-fold Cross Validartion, by testing several K values to get the K value with the highest accuracy. The value of K=4 gets the highest accuracy of 99.6088%. The results of this research is a computer classification management information system in the form of a web. This research is expected to make it easier for Bank X to determine which computers need and do not need to be replaced, in order to be able to make effective decisions in computer replacement.]]>
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