Ensemble Method Builds a Predictive by Integrating Several Models for Accurate Answer Prediction on Chatbot
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Abstract
One of the technological advances that has had a positive impact is the Chatbot. A chat service which is actually a feature that has been very often used by tech-savvy people. But the difference is, the ones who reply in the chat process are robots or virtual characters. The chatbot will provide answers to the questions given to it which is basically the chatbot assigned to understand what context the user (user) means, then reply to it with the appropriate context. However, each context has a different input, human language has a very flexible way so that it is often found inaccuracies in the prediction of answers given by Chatbot. This could be due to the incorrect choice of algorithms for the classification of the context or the lack of training data provided. To overcome this, this study will focus on strengthening the prediction of chatbot answers with the method of the machine learning algorithm (Machine Learning) ensemble 5 classification technique heterogeneously between basic classifiers and meta-algorithms and using maximum voting (Majority Vote) or Hard Voting on type ensemble. Classification is the process of finding a model or pattern that can describe and differentiate classes in a dataset. The goal is that the model can be used to predict objects with unknown class labels. It was found that the accuracy results were 86% for the data set with 6 classes, the avg Macro for each precision and recall was 92%, and the f1-score was 89%. Weighted avg for precision is 93% and each for recall and f1-score is 86%
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