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Comparison study of neural network and deep neural network on repricing GAP prediction in Indonesian conventional public bank
Karisma H.a, Widyantoro D.H.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016 IEEE.The economic condition of a country is determined by the bank condition of that country it self, so that’s why the whole world keep looking for the most effective and efficient method in conducting the assessment of the bank performance condition. Risk rate is commonly used to perform an analysis of the health condition of the bank, mainly using repricing gap. This research is propose deep neural network (DNN) which is one of deep learning algorithm as a method to predict the value of the repricing gap of the bank. The experiments is do a performance comparison of two methods that are neural network – standard backpropagation (SB) and DNN, by using 10 years historical data of monthly report. In DNN is using autoencoder which is an unsupervised algorithm as initiator value gradient on the formation of the network. The experimental results of both methods is the performance of DNN is better than SB. Using 30700 iteration, obtained the MSE value for DNN is more lower than SB. At high iteration amount, DNN still consistent to press the MSE value, and it’s different with SB that start slowing down at the earliest iteration point, about 5700 iteration. DNN network topology that used in the experiments is the topology that can produce a model that have the most minimum MSE value, and that is equal network topology. (Every hidden layer neuron has the same number of units).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]bank,Economic condition,Health condition,Hidden layer neurons,Network standards,Performance comparison,repricing gap,Unsupervised algorithms[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]backpropagation,bank,deep learning,deep neural network,dnn,prediction,repricing gap,risk[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/FIT.2016.7857549[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]