PERBANDINGAN ALGORITMA NAIVE BAYES DENGAN REGRESI LOGISTIK UNTUK KLASIFIKASI HATE SPEECH

YULIANSYAH, DERI and Jambak, M. Ihsan and Yunita, Yunita (2020) PERBANDINGAN ALGORITMA NAIVE BAYES DENGAN REGRESI LOGISTIK UNTUK KLASIFIKASI HATE SPEECH. Undergraduate thesis, Sriwijaya University.

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Abstract

Text classification or document classification is one of the main tasks in text data mining and information retrieval. Classification is used to predict group membership against an instance of data. Hate Speech is an act of communication that vilifies individual targets or specific group characteristics such as race, ethnicity, gender, religion, and others. The Naïve Bayes Algorithm is an algorithm based on the concept of Bayes probability, while logistic regression is a model used to find the relationship between 2 variables, namely two-category response variables or dichotomous variables or variables with two or more categories which are polychotomous with one or more predictors on a category or continuous scale. In this study, a comparison between the Naïve Bayes algorithm and Logistic Regression was carried out to classify hate speech. This study aims to compare the accuracy and computation time of the algorithms used. Keywords: Hate speech classification, Naïve Bayes Algorithm, Regression Algorithm Logistics, Accuracy, Computing Time

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Naive Bayes, Regresi Logistik
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:	Users 7804 not found.
Date Deposited:	04 Sep 2020 03:28
Last Modified:	04 Sep 2020 03:28
URI:	http://repository.unsri.ac.id/id/eprint/34450

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