Heart disease is one of serious ailments in most countries especially in Indonesia. Based on Indonesia Heart Foundation reports, there is almost 27 heart disease sufferers in each 100 persons. This rate is the second highest rate of death cause in Indonesia. In addition, the foundation reported that the one who suffered by this illness are youngsters. Early detection is certainly needed in order to reduce the death rate caused by heart failure. Intelligent Systems is one of detection methods that can help in decision making accuracy related to heart disease early detection.In this research, intelligent system is built by combining decision tree algorithm with fuzzy clustering system. Due to accuracy testing of this combination performance, UCI dataset in Machine Learning Repository was used to categorize the range of heart failure seriousness started from low rate up to high rate of severity. The AUC (Area under ROC) showed 87.8% as its results which means that decision tree classifier algorithm has a good classification performance by grouping the testing data 87 instance data correctly out of 100. Finally, the long-term objective of this research is to help doctors as a second opinion for early heart diagnoses.