Melola Discovery

← Melola Discovery

Journal

Optimalisasi Algoritma Naïve Bayes untuk Klasifikasi Tweet Berbahasa Indonesia dalam Mengatasi Hate Speech di Platform X

Indonesian Journal of Mathematics and Natural Sciences · Vol. 47 No. 2 (2024) · pp. 109–114

DOI
10.15294/zphjtr75
ISSN
2774-7832
Published
2024-10-22
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Hate speech is a form of expression used to express hatred and is often destructive, aimed at opposing individuals or certain groups for various reasons. Cases of hate speech are frequently found on social media, especially during election seasons, which occur regularly. To combat hate speech, monitoring actions are needed by censoring words that have the potential to offend and attack personal elements, such as ethnicity, religion, and race. Previous research has generally focused only on sentiment analysis of tweets to determine their positive or negative weights. This research continues the study on hate speech by developing the application of the Naive Bayes algorithm, specifically the Multinomial and Gaussian variants, along with an automatic censorship system aimed at improving classification accuracy. This system is implemented on social media with the hope of significantly reducing the amount of hate speech. From 13,169 tweets collected as the dataset, the data was classified into 12 categories with the highest accuracy rate being 90%. The test results are stored in the form of a hate speech dictionary that contains inappropriate words, allowing the algorithm to detect and automatically censor tweets containing hate speech

Keywords

Collaboration network of the authors

40 authors · 112 co-authorship links · 7 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
  • Medan Area University
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Amalia Ika WulandariAffiliation unknown4
Much Aziz MuslimTun Hussein Onn University of Malaysia3
Endang SugihartiState University of Semarang3
Muhammad Uswah PawaraAffiliation unknown3
Rodlian Jamal IkhwaniAffiliation unknown3
Riza ArifudinState University of Semarang2
Chris Jeremy Verian SitorusAffiliation unknown2
Suardi SuardiAffiliation unknown2
Sufandi IswantoAffiliation unknown2
Teuku KusnafizalAffiliation unknown2
Kholiq BudimanAffiliation unknown2
Muhamad Faishol HakimAffiliation unknown2
Meidika Bagus SaputroAffiliation unknown2
Diah Siti Fatimah AzzahrahAffiliation unknown2
Lisa AriyantiAffiliation unknown2
Budi PrasetiyoState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Agus KurniaUniversitas Halu Oleo1
Wellem H. MuskitaAffiliation unknown1
Charles Sambera KalaAffiliation unknown1
I Putu Arya KusumaAffiliation unknown1
Mursid Nugraha ArifuddinAffiliation unknown1
Niti Gede KumandangAffiliation unknown1
Taufik HidayatMedan Area University1
Arif Nur IskandarAffiliation unknown1
Abrari Noor HasmiAffiliation unknown1
Asram Dio PratamaAffiliation unknown1
Irvan SetiawanAffiliation unknown1
Destyariani Liana PutriAffiliation unknown1
Alan Dicky Swans SaragihAffiliation unknown1
Anggoronadhi DianiswaraAffiliation unknown1
Wira SetiawanAffiliation unknown1
Andi Mursid Nugraha ArifuddinAffiliation unknown1
Samsu Dlukha NurcholiqAffiliation unknown1
SuardiAffiliation unknown1
Harlian KustiwansaAffiliation unknown1
Riza AbdillahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/JM/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Optimalisasi Algoritma Naïve Bayes untuk Klasifikasi Tweet Berbahasa Indonesia dalam Mengatasi Hate Speech di Platform X</dc:title>
	<dc:creator xml:lang="en">Haikal, Muhammad </dc:creator>
	<dc:creator xml:lang="en">Alamsyah, Alamsyah</dc:creator>
	<dc:subject xml:lang="en">Hate speech detection</dc:subject>
	<dc:subject xml:lang="en">Naïve Bayes</dc:subject>
	<dc:subject xml:lang="en">tweet classification</dc:subject>
	<dc:description xml:lang="en">Hate speech is a form of expression used to express hatred and is often destructive, aimed at opposing individuals or certain groups for various reasons. Cases of hate speech are frequently found on social media, especially during election seasons, which occur regularly. To combat hate speech, monitoring actions are needed by censoring words that have the potential to offend and attack personal elements, such as ethnicity, religion, and race. Previous research has generally focused only on sentiment analysis of tweets to determine their positive or negative weights. This research continues the study on hate speech by developing the application of the Naive Bayes algorithm, specifically the Multinomial and Gaussian variants, along with an automatic censorship system aimed at improving classification accuracy. This system is implemented on social media with the hope of significantly reducing the amount of hate speech. From 13,169 tweets collected as the dataset, the data was classified into 12 categories with the highest accuracy rate being 90%. The test results are stored in the form of a hate speech dictionary that contains inappropriate words, allowing the algorithm to detect and automatically censor tweets containing hate speech</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-10-22</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/JM/article/view/15322</dc:identifier>
	<dc:identifier>10.15294/zphjtr75</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Mathematics and Natural Sciences; Vol. 47 No. 2 (2024): Volume 47 Nomor 2 Tahun 2024; 109-114</dc:source>
	<dc:source>2774-7832</dc:source>
	<dc:source>10.15294/57dzv753</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/JM/article/view/15322/1746</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Indonesian Journal of Mathematics and Natural Sciences</dc:rights>
</oai_dc:dc>