  Indonesian is an agglutinative language since it has a compounding process of
word-formation. Therefore, the translation model of this language requires a
mechanism that is even lower than the word level, referred to as the sub-word
level. This compounding process leads to a rare word problem since the number
of vocabulary explodes. We propose a strategy to address the unique word
problem of the neural machine translation (NMT) system, which uses Indonesian
as a pair language. Our approach uses a rule-based method to transform a word
into its roots and accompanied affixes to retain its meaning and context. Using
a rule-based algorithm has more advantages: it does not require corpus data but
only applies the standard Indonesian rules. Our experiments confirm that this
method is practical. It reduces the number of vocabulary significantly up to
57\%, and on the English to Indonesian translation, this strategy provides an
improvement of up to 5 BLEU points over a similar NMT system that does not use
this technique.
