  In this paper, MapReduce programming model is used to parallelize training
and tagging proceess in Maximum Entropy part of speech tagging for Bahasa
Indonesia. In training process, MapReduce model is implemented dictionary,
tagtoken, and feature creation. In tagging process, MapReduce is implemented to
tag lines of document in parallel. The training experiments showed that total
training time using MapReduce is faster, but its result reading time inside the
process slow down the total training time. The tagging experiments using
different number of map and reduce process showed that MapReduce implementation
could speedup the tagging process. The fastest tagging result is showed by
tagging process using 1,000,000 word corpus and 30 map process.
