Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning alg...

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Autor principal:	Suwarno Liang
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	hate speech classification machine learning svm xgboost neural network Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/5fcf6029a49c4f0e852b30fcba14cc8f
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id	oai:doaj.org-article:5fcf6029a49c4f0e852b30fcba14cc8f
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spelling	oai:doaj.org-article:5fcf6029a49c4f0e852b30fcba14cc8f2021-11-16T13:16:12ZComparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification2580-076010.29207/resti.v5i5.3506https://doaj.org/article/5fcf6029a49c4f0e852b30fcba14cc8f2021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3506https://doaj.org/toc/2580-0760In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning algorithms. This study is to analyze the level of accuracy, precision, recall and F1-Score of 3 kinds of algorithms (SVM, XGBoost, and Neural Network) in the classification of hate speech, using datasets sourced from public hate speech on Twitter in Indonesian. The results of the analysis show that the SVM algorithm has a level of accuracy (83.2%), precision (83%), recall (83%) and F1-score (83%), SVM occupies the highest level compared to XGBoost and Neural Network, so the SVM algorithm can be considered for use in hate speech classificationSuwarno LiangIkatan Ahli Indormatika Indonesiaarticlehate speech classificationmachine learningsvmxgboostneural networkSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 896-903 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	hate speech classification machine learning svm xgboost neural network Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	hate speech classification machine learning svm xgboost neural network Systems engineering TA168 Information technology T58.5-58.64 Suwarno Liang Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification
description	In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning algorithms. This study is to analyze the level of accuracy, precision, recall and F1-Score of 3 kinds of algorithms (SVM, XGBoost, and Neural Network) in the classification of hate speech, using datasets sourced from public hate speech on Twitter in Indonesian. The results of the analysis show that the SVM algorithm has a level of accuracy (83.2%), precision (83%), recall (83%) and F1-score (83%), SVM occupies the highest level compared to XGBoost and Neural Network, so the SVM algorithm can be considered for use in hate speech classification
format	article
author	Suwarno Liang
author_facet	Suwarno Liang
author_sort	Suwarno Liang
title	Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification
title_short	Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification
title_full	Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification
title_fullStr	Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification
title_full_unstemmed	Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification
title_sort	comparative analysis of svm, xgboost and neural network on hate speech classification
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/5fcf6029a49c4f0e852b30fcba14cc8f
work_keys_str_mv	AT suwarnoliang comparativeanalysisofsvmxgboostandneuralnetworkonhatespeechclassification
_version_	1718426482222563328

Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

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