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Lstm and Simple Rnn Comparison in the Problem of Sequence to Sequence on Conversation Data Using Bahasa Indonesia
Prabowo Y.D.a,b, Warnars H.L.H.S.a, Budiharto W.a, Kistijantoro A.I.c, Heryadi Y.a, Lukasd
a Computer Science Departement, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia
b Informatics Departement, Institut Bisnis Dan Teknologi Kalbis, Jakarta, 13210, Indonesia
c School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
d Cognitive Engineering Research Group, Faculty of Engineering, Universitas Katolik Indonesia Atma Jaya, Jakarta, 12930, 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]© 2018 IEEE.This study aims to implement and compare the Long Short Term Memory (LSTM) and Simple Recurrent Neural Networks (RNN) algorithm in the case of chatbot using Bahasa Indonesia data. The chatbot model used is a cahatbot model across business/service fields. The training data used in this research are the data on customer service talks with its customers in several business fields or services. To compare the models generated from the LSTM algorithm and Simple RNN algorithm, two tests were carried out, the first test is testing the chat output manually which was done directly by humans and the second test are comparing the LSTM algorithm and Simple RNN algorithms using the same training data and test data. From the experimental results, it was found that the chat output generated by the LSTM algorithm relatively can answer most of the tests correctly rather than Simple RNN algorithm. From the experiment, it was also found that the learning process in the LSTM algorithm takes longer than the learning process on the Simple RNN algorithm.[/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]Chatbot,Customer services,Indonesia,Learning process,LSTM,Simple recurrent neural networks,Test data,Training data[/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]Bahasa Indonesia,Chatbot,LSTM,RNN[/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/INAPR.2018.8627029[/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]