We propose a medical named entity recognition for medical question answering system with Indonesian language. The aim is to provide a good medical named entity grammar by only using the available language resource. Our strategy here is to build the features most often used for the recognition and classification of medical named entities. We organize them along two different axes: word-level and list features, document and corpus features. For the reason we built our own features to Indonesian medical named entities and used it as the feature of the available with SVM Software. By using 3000 sentences, the highest accuracy score achieved is about 90%.