  Abstract Meaning Representation (AMR) provides many information of a sentence
such as semantic relations, coreferences, and named entity relation in one
representation. However, research on AMR parsing for Indonesian sentence is
fairly limited. In this paper, we develop a system that aims to parse an
Indonesian sentence using a machine learning approach. Based on Zhang et al.
work, our system consists of three steps: pair prediction, label prediction,
and graph construction. Pair prediction uses dependency parsing component to
get the edges between the words for the AMR. The result of pair prediction is
passed to the label prediction process which used a supervised learning
algorithm to predict the label between the edges of the AMR. We used simple
sentence dataset that is gathered from articles and news article sentences. Our
model achieved the SMATCH score of 0.820 for simple sentence test data.
