KLASIFIKASI INTENT PADA CHATBOT TERAPI MENGGUNAKAN MULTINOMIAL NAIVE BAYES DAN MUTUAL INFORMATION

ARISYA, NUR SANIFA and Abdiansah, Abdiansah (2021) KLASIFIKASI INTENT PADA CHATBOT TERAPI MENGGUNAKAN MULTINOMIAL NAIVE BAYES DAN MUTUAL INFORMATION. Undergraduate thesis, Sriwijiaya University.

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Abstract

Chatbot is a program that designed to be able to interact with humans through messages or voice. The chatbot will identify the intent that will understand and recognize the statements that the user enters into the chatbot. However, the problem that often arises in chatbots is that sometimes chatbots respond to inappropriate dialogue interactions. Therefore, the classification of intents is one way to be able to categorize an intent that helps provide an appropriate response to dialogue interactions. The Multinomial Naive Bayes (MNB) method is a method that is widely used, especially in document classification. However, the more data, the more features will be processed, so the MNB process will take longer. To overcome this problem, the Mutual Information method is used to reduce the number of features. The purpose of this study was to determine the performance of the MNB classification by selecting the Mutual Information feature and the MNB classification method without selecting the Mutual Information feature. The results of the tests that have been carried out show that the MNB method by selecting the Mutual Information feature has better performance than the MNB method without the Mutual Information feature selection with an accuracy of  0,27 % and 0,53 %

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Intent Classification, Mutual Information, Stres, Multinomial Naive Bayes, Raelated Discussion
Subjects: P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Nur Sanifa Arisya
Date Deposited: 22 Sep 2021 02:00
Last Modified: 22 Sep 2021 02:00
URI: http://repository.unsri.ac.id/id/eprint/54113

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