<![CDATA[Hadoop is a framework based on an open-source which is used for a data processing in a large scale and stored in a group of interconnected computers that are connected in a network which are distributed. Hadoop there are several scheduling algorithms to set the incoming job queue, jobs that have been submitted by the user will compete for a resource available. So the need for a scheduling algorithm in charge of managing the job on a resource and managing the data processing so that output issued in accordance with what is expected. This research was conducted to find out the comparison of performance of Fair Share Scheduling and Capacity Scheduling algorithm on job delivery on Hadoop multinode to know the value of Job Fail Rate, Latency, and Throughput parameters. Capacity Scheduling supports the queue in a hierarchy that is able to divide the available resources in the cluster into multiple queues. Fair Share Scheduling is a fair allocation of resource allocation methods for queues for all jobs that enter the queue. Based on the results of tests that have been done, has a minimum value of Job Failed Rate is better than Fair Share Scheduling of 0.99%. Fair Share Scheduling has a minimum value of better Latency of 19.25 minutes. While the value of Throughput on Fair Share Scheduling faster by 0.47 Mbit/s.]]>
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