Emotion is a vital component in various Affective Computing areas such as opinion mining, sentiment analysis, e-learning applications, human-computer interaction and humor recognition. In this paper, we propose a two-stage approach for detecting emotions on Indonesian tweets. In the first stage, we extract emotion-bearing tweets from a huge number of raw tweets. In the second stage, all the extracted tweets are then classified into five well-known pre-defined emotion classes, namely love, joy, sad, fear, and anger. To do that, we devise various features (i.e., linguistic, semantic, and orthographic features) and subsequently use those proposed features to build a computational model based on machine learning approach. Our experimental results show that the proposed method is very effective. It is also worth noting that the work described in this paper is the first work on emotion analysis on Indonesian data.