Identifikasi Ujaran Kebencian Etnis Dan Agama Menggunakan Algoritma SVM

Authors

  • Bayu Ardiansyah Universitas Nusantara PGRI Kediri Indonesia
  • Risky Aswi Ramadhani Universitas Nusantara PGRI Kediri Indonesia
  • Julian Sahertian Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/hzvjh917

Abstract

Perkembangan media sosial yang semakin pesat turut meningkatkan penyebaran ujaran kebencian berbasis etnis dan agama. Penelitian ini bertujuan untuk menganalisis tingkat akurasi algoritma Support Vector Machine (SVM) dalam mendeteksi ujaran kebencian pada data teks media sosial. Dataset diperoleh melalui teknik web scraping pada platform Twitter (X) dan menghasilkan 1.769 data teks. Data kemudian melalui tahap preprocessing yang meliputi case folding, tokenizing, stopword removal, dan stemming. Pelabelan data dilakukan menggunakan pendekatan lexicon-based classification, sedangkan ekstraksi fitur menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Model SVM selanjutnya dilatih dan dievaluasi menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa model mampu mengklasifikasikan data ujaran kebencian dan non-ujaran kebencian dengan akurasi sebesar 88%. Hasil tersebut menunjukkan bahwa kombinasi preprocessing, TF-IDF, dan SVM efektif dalam mendukung deteksi otomatis ujaran kebencian berbasis etnis dan agama pada media sosial.

Keywords:

ujaran kebencian, support vector machine, TF-IDF

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References

[1] W. A. Social and Meltwater, "Digital 2024: Indonesia report," 2024. [Online]. Available: https://datareportal.com/reports/digital-2024-indonesia

[2] D. A. F. Sarie, Suhartono, and Mintowati, "Ujaran Kebencian Di Media Sosial (Kajian Pragmasemantik)," J. Educ. Dev., vol. 9, no. 4, pp. 247-251, 2021.

[3] E. W. Pamungkas, D. G. P. Putri, and A. Fatmawati, "Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities," Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 6, pp. 1175-1181, 2023, doi: 10.14569/IJACSA.2023.01406125.

[4] M. O. Ibrohim and I. Budi, "Hate speech and abusive language detection in Indonesian social media: Progress and challenges," Heliyon, vol. 9, no. 8, p. e18647, 2023, doi: 10.1016/j.heliyon.2023.e18647.

[5] N. Aulia and I. Budi, "Hate speech detection on Indonesian long text documents using machine learning approach," ACM Int. Conf. Proceeding Ser., no. April 2019, pp. 164-169, 2019, doi: 10.1145/3330482.3330491.

[6] K. Hadi and E. Utami, "Analysis of K-NN with the Integration of Bag of Words, TF-IDF, and N-Grams for Hate Speech Classification on Twitter," JUITA J. Inform., vol. 12, no. 2, p. 289, 2024, doi: 10.30595/juita.v12i2.23829.

[7] S. A. Helmayanti, F. Hamami, and R. Y. Farifah, "Penerapan Algoritma TF-IDF dan Naïve Bayes untuk Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Flip pada Google Play Store," J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 3, pp. 1822-1834, 2023, doi: 10.35870/jimik.v4i3.415.

[8] N. M. Al Ghazali and Y. Sibaroni, "Sentiment Classification in E-Commerce Using Naïve Bayes and Combined Lexicon - N-Gram Features," JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform.), vol. 10, no. 2, pp. 1257-1271, 2025, doi: 10.29100/jipi.v10i2.6157.

[9] I. Riadi, A. Fadlil, and M. Murni, "Identifying Hate Speech in Tweets with Sentiment Analysis on Indonesian Twitter Utilizing Support Vector Machine Algorithm," Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 9, no. 2, pp. 179-191, 2023, doi: 10.23917/khif.v9i2.22470.

[10] K. M. Suryaningrum, "Comparison of the TF-IDF Method with the Count Vectorizer to Classify Hate Speech," Eng. Math. Comput. Sci. J., vol. 5, no. 2, pp. 79-83, 2023, doi: 10.21512/emacsjournal.v5i2.9978.

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Published

2026-07-20

Issue

Vol. 10 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

Section

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How to Cite

Identifikasi Ujaran Kebencian Etnis Dan Agama Menggunakan Algoritma SVM. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(1), 188-196. https://doi.org/10.29407/hzvjh917