Abstract- Indonesia is a country located between the Indo-Australian, Euresian and the pacific plate. Based on these facts, earthquakes are frequent in Indonesia, especially in Sumatra. Therefore, an early detection of an earthquake, also known as an earthquake precursor, is required. At the moment, some research is exploring the earthquake relation with total electron content located in ionosphere. Machine learning methods and artificial intelligence are used to detect earthquake precursors. This study focusses on the N-ANN (N-Model Neural Network Model) Method for detecting earthquake precursors, this study uses the Dst (Disturbance Storm Time) Index ro subtract the effects of geomagnetic strom from TEC. TEC data uses TEC GIM (Global Ionospheric Maps) at 00:00. The observed earthquakes were the December 2004 to March 2005 earthquakes. The experiment show that N-ANN is more stable with the 5 model ANN, 3 hidden layer and 2 neurons. Earthquakes precursors found 3 to 0 days before the earthquake occurred. The experimental results on 16 earthquake events reach 76% accuracy, 81% recall and 93% precision. It can be concluded that N-ANN can be considered to detect earthquake precursors for early detection of earthquakes as a warning system.