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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) · hlm. 109–114

DOI
10.15294/zphjtr75
ISSN
2774-7832
Terbit
2024-10-22
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

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

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  • Institut Teknologi Kalimantan
  • State University of Semarang
  • National Research and Innovation Agency
  • Afiliasi tidak diketahui
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  • Organisasi sama
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Alamsyah AlamsyahInstitut Teknologi Kalimantan7
Much Aziz MuslimState University of Semarang3
Endang SugihartiState University of Semarang3
Amalia Ika WulandariAfiliasi tidak diketahui3
Riza ArifudinState University of Semarang2
Chris Jeremy Verian SitorusInstitut Teknologi Kalimantan2
Muhammad Uswah PawaraInstitut Teknologi Kalimantan2
Rodlian Jamal IkhwaniInstitut Teknologi Kalimantan2
Suardi SuardiAfiliasi tidak diketahui2
Amalia Ika WulandariInstitut Teknologi Kalimantan2
Suardi SuardiInstitut Teknologi Kalimantan2
Lisa AriyantiState University of Semarang2
Budi PrasetiyoState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Wellem H. MuskitaAfiliasi tidak diketahui1
Agus KurniaAfiliasi tidak diketahui1
Charles Sambera KalaAfiliasi tidak diketahui1
Niti Gede KumandangInstitut Teknologi Kalimantan1
Arif Nur IskandarAfiliasi tidak diketahui1
Taufik HidayatNational Research and Innovation Agency1
Taufik HidayatInstitut Teknologi Kalimantan1
Arif Nur IskandarInstitut Teknologi Kalimantan1
Abrari Noor HasmiAfiliasi tidak diketahui1
Abrari Noor HasmiInstitut Teknologi Kalimantan1
Asram Dio PratamaInstitut Teknologi Kalimantan1
Rodlian Jamal IkhwaniAfiliasi tidak diketahui1
Irvan SetiawanInstitut Teknologi Kalimantan1
Destyariani Liana PutriAfiliasi tidak diketahui1
Alan Dicky Swans SaragihAfiliasi tidak diketahui1
Anggoronadhi DianiswaraAfiliasi tidak diketahui1
Destyariani Liana PutriInstitut Teknologi Kalimantan1
Alan Dicky Swans SaragihInstitut Teknologi Kalimantan1
Anggoronadhi DianiswaraInstitut Teknologi Kalimantan1
Wira SetiawanAfiliasi tidak diketahui1
Muhammad Uswah PawaraAfiliasi tidak diketahui1
Andi Mursid Nugraha ArifuddinAfiliasi tidak diketahui1
Samsu Dlukha NurcholiqAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/JM/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<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>