Semantic role labeling (SRL) is a task to as- sign semantic role labels to sentence elements. This pa- per describes the initial development of an Indonesian semantic role labeling system and its application to extract event information from Tweets. We compare two feature types when designing the SRL systems: Word-to-Word and Phrase-to-Phrase. Our experiments showed that the Word- to-Word feature approach outperforms the Phrase-to-Phrase approach. The application of the SRL system to an event extraction problem resulted overlap-based accuracy of 0.94 for the actor identification.