High Accuracy Conversational AI Chatbot Using Deep Recurrent Neural Networks Based on BiLSTM Model

Abstract

In the modern world, chatbot programs are implementations that can be used to store data collected through a question and answer system and then can be applied in the Python program to optimize the results based on highly rated questions asked in a service center. The application of chatbots in the Python program can use various models. Specifically in this program, the BiLSTM model will be applied. The output produced from the chatbot program with the application of the BiLSTM model is in the form of accuracy and also data set that matches the information the program user enters in the chatbot's input dialog box. The selection of models that can be applied to the program is based on data which can affect program performance, with the objective of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in deciding the selected model. Based on the various considerations that are the requirements for choosing a model of a program, in the end the BiLSTM model is selected will be applied to the program. In addition to model selection, the next step is to determine the method used in the program, in this program the greedy method is a form of implementation of the BiLSTM model with the aim that when running the program, data processing time can be faster, and increase the value of the model selected in program. In addition, supporting attributes such as the seq2seq model are a determining factor in a program that can function to verify whether data processing matches the criteria that can be used as new in data processing. In addition, a program evaluation method is needed that can be used to verify whether the program output matches the data expected by the user. Based on the application of the BiLSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size-layer 512, num-layers 2, embedded-size 256, learning-rate 0.001, batch-size 32, epoch 20) from File 3 is the BiLSTM Chatbot with the avg accuracy value of 0.995217 which uses the BiLSTM model is the best parameter pair.