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Analisis Perbandingan Algoritma Naïve Bayes dan Support Vector Machine dalam Klasifikasi Opini dan Fakta pada Berita Banjir Sumatera

  • Dessy Natalia Reba1*
  • Lilis Indrayani2
  • Christian Dwi Suhendra3
1,2,3 State University of Papua image/svg+xml
* Corresponding Author: Dessy Natalia Reba

Abstract

The rapid growth of online media has accelerated the dissemination of information related to flood disasters, creating the need for an automatic method to classify news into factual and opinion categories. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying factual and opinion-based news on flood events in Sumatra using Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The research employed several text preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming, followed by TF-IDF weighting, classification, and model evaluation using accuracy, precision, recall, and F1-score. The experimental results showed that the Naïve Bayes algorithm achieved an accuracy of 92.34%, outperforming the Support Vector Machine algorithm, which achieved an accuracy of 91.88%. In addition, Naïve Bayes obtained higher precision, recall, and F1-score values for the opinion class. These findings indicate that Naïve Bayes is more effective than Support Vector Machine in classifying factual and opinion-based news related to flood events in Sumatra.

Keywords

Text Mining Naïve Bayes Support Vector Machine TF-IDF News Classification

How to Cite

Citation Format
Reba, D. N., Indrayani, L., & Suhendra, C. D. (2027). Analisis Perbandingan Algoritma Naïve Bayes dan Support Vector Machine dalam Klasifikasi Opini dan Fakta pada Berita Banjir Sumatera. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 11(1), 191-201. https://doi.org/10.35870/jtik.v11i1.7526

References

15 References
  • Agustin, Y. H., Mulyani, N. C., & Prasetya, W. S. (2025). Analisis sentimen opini publik menggunakan algoritma Naïve Bayes dan TF-IDF. Jurnal Algoritma, 22(2), 1373–1384.
  • Bangun, E. P., Koagouw, F. V. I. A., & Kalangi, J. S. (2019). Analisis isi unsur kelengkapan berita pada media online ManadoPostOnline.com. Jurnal Ilmiah, 1(1), 1–13.
  • Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan algoritma Naïve Bayes untuk analisis sentimen review data Twitter BMKG Nasional. Jurnal Tekno Kompak, 15(1), 131–145.
  • Deolika, A., Kusrini, & Luthfi, E. T. (2019). Analisis pembobotan kata pada klasifikasi text mining. Jurnal Teknologi Informasi, 3(2), 179–184.
  • Habib, S. M., Haerani, E., Gusti, S. K., & Ramadhani, S. (2022). Klasifikasi berita menggunakan metode Naïve Bayes classifier. Jurnal Nasional Komputasi dan Teknologi Informasi, 5(2), 248–258.
  • Hermawan, L., & Ismiati, M. B. (2020). Pembelajaran text preprocessing berbasis simulator untuk mata kuliah information retrieval. TRANSFORMATIKA, 17(2), 188–199.
  • Khairunnisa, S., Adiwijaya, A., & Al Faraby, S. (2021). Pengaruh text preprocessing terhadap analisis sentimen komentar masyarakat pada media sosial Twitter (studi kasus pandemi COVID-19). Jurnal Media Informatika Budidarma, 5(2), 406–414.
  • Kowsari, K., Meimandi, K. J., Heidarysafa, M., Mendu, S., Barnes, L. E., & Brown, D. E. (2019). Text classification algorithms: A survey. Information, 10(4), Article 150. https://doi.org/10.3390/info10040150.
  • Krisnandi, D., Ambarwati, R. N., Asih, A. Y., Ardiansyah, A., & Pardede, H. F. (2023). Analisis komentar cyberbullying terhadap kata yang mengandung toksisitas dan agresi menggunakan bag of words dan TF-IDF dengan klasifikasi SVM. Jurnal Linguistik Komputasional, 6(2), 36–41.
  • Lestari, R., Sudiyana, B., & Wahyuni, T. (2019). Fakta dan opini dalam teks tajuk rencana pada surat kabar Kompas. KLITIKA: Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia, 1(1), 1–10.
  • Mettler, M., & Mondak, J. J. (2024). Fact-opinion differentiation. Harvard Kennedy School Misinformation Review, 5(2). https://doi.org/10.37016/mr-2020-136.
  • Nanda, R., Haerani, E., Gusti, S. K., & Ramadhani, S. (2022). Klasifikasi berita menggunakan metode support vector machine. Jurnal Nasional Komputasi dan Teknologi Informasi, 5(2), 269–278.
  • Putri, A. K., & Nur, D. I. (2023). Penggunaan bahasa Python untuk analisis dan visualisasi data penduduk di Desa Sumberjo, Nganjuk. KARYA: Jurnal Pengabdian kepada Masyarakat, 3(3), 206–217.
  • Rahmatika, N., & Prisanto, G. F. (2022). Pengaruh berita clickbait terhadap kepercayaan pada media di era attention economy. Avant Garde, 10(2), 190–200.
  • Shidiq, M. F. A., & Alita, D. (2025). Analisis sentimen masyarakat terhadap kasus judi online menggunakan data dari media sosial X pendekatan Naïve Bayes dan SVM. Jurnal Sistem Informasi dan Informatika (SIMIKA), 8(1), 24–35.

Author Biographies

Dessy Natalia Reba, State University of Papua

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Papua, Kabupaten Manokwari, Provinsi Papua Barat, Indonesia.

Lilis Indrayani, State University of Papua

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Papua, Kabupaten Manokwari, Provinsi Papua Barat, Indonesia.

Christian Dwi Suhendra, State University of Papua

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Papua, Kabupaten Manokwari, Provinsi Papua Barat, Indonesia.

License

Copyright (c) 2027 Dessy Natalia Reba, Lilis Indrayani, Christian Dwi Suhendra

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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