Deep Learning, developed from the multi-layer ANN concept, is a machine learning method that compiles every detail of the learning process to obtain more abstract data, multi-level data, and more complex data features through the composition of various mathematical functions. Deep MLP, another name for DNN (Deep Neural Network), is MLP, which has more than three layers. DNN's ability will increase in solving problems when more layers are used. Another advantage of DNN is the variety of layer types used, including fully connected layers, convolution layers, softmax layers, recurrent layers, and others. Autoencoder is a type of ANN that trained to reconstruct input patterns in such a way that the output of the deepest hidden layer is a vector resulting from the reduction of dimensions of the input pattern. This study proposes a novel DNN architecture called Deep Auto-Encoder Semi Convolutional Neural Network (DAESCNN) to improve the performance of conventional ANN performance. Annual rainfall data will be used to test forecast performance using DAESCNN. In this study, annual rainfall data were obtained from weather stations of Samarinda city, East Kalimantan, Indonesia in the period 2006-2016. In this study, all 2006-2014 data points were used as training input data, all 2007-2015 data points were used as training target data, while 2016 data points were used as validation of training outcomes. The DAESCNN training process executed through a program built using MATLAB. The training process also carried out with varying training parameters to show its performance. The results shown that DAESCNN generally has excellent performance, is above 99%.