Lstm And Simple Rnn Comparison In The Problem Of Sequence To Sequence On Conversation Data Using Bahasa Indonesia

Abstract

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