A model to convert a grapheme into a phoneme (G2P) is crucial in the natural language processing area. In general, it is developed using a probabilistic-based data-driven approach and directly applied to a sequence of graphemes with no other information. Important research shows that incorporating information of syllabification point is capable of improving a probabilistic-based English G2P. However, the information should be accurately provided by a perfect orthographic syllabification. Some noises or errors of syllabification significantly reduce the G2P performance. In this paper, incorporation of syllabification points into a probabilistic-based G2P model for Bahasa Indonesia is investigated. This information is important since Bahasa Indonesia is richer than English in terms of syllables. A 5-fold cross-validating on 50 k words shows that the incorporation of syllabification points significantly improves the performance of G2P model, where the phoneme error rate (PER) can be relatively reduced by 10.75%. This PER is much lower than the G2P model based on an inductive learning algorithm. An important contribution of this research is that the proposed G2P model is quite robust to syllabification errors. A syllable error rate (SER) of 2.5% that comes from an orthographic syllabification model just slightly increases the PER of the proposed G2P model from 0.83% to be 0.90%. A higher SER up to 10% just increase the PER to be 1.14%.