Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia

Ramadhan Rakhmat Sani; Yunita Ayu Pratiwi; Sri Winarno; Erika Devi Udayanti; Farrikh Alzami

doi:10.14710/jmasif.13.2.47983

DOI: https://doi.org/10.14710/jmasif.13.2.47983

Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia

Ramadhan Rakhmat Sani , Yunita Ayu Pratiwi, Sri Winarno, Erika Devi Udayanti, Farrikh Alzami

Universitas Dian Nuswantoro, Indonesia

Received: 29 Jul 2022; Revised: 10 Oct 2022; Accepted: 11 Oct 2022; Available online: 8 Nov 2022; Published: 8 Nov 2022.

Editor(s): Panji Wirawan

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

BibTex Citation Data :

@article{JMASIF47983,
    author = {Ramadhan Sani and Yunita Pratiwi and Sri Winarno and Erika Udayanti and Farrikh Alzami},
    title = {Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia},
    journal = {Jurnal Masyarakat Informatika},
  volume = {13},
    number = {2},
    year = {2022},
    keywords = {Naïve Bayes Classifier; Support Vector Machine; Klasifikasi Berita Hoax; Berita Hoax; TF-IDF},
    abstract = { Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan. },
   issn = {2777-0648},   pages = {85--98}  doi = {10.14710/jmasif.13.2.47983},
    url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/47983}
}

Citation Format:

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.

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Keywords: Naïve Bayes Classifier; Support Vector Machine; Klasifikasi Berita Hoax; Berita Hoax; TF-IDF

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Section: Research Article

Language : ID

In Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA

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V. B. Kusnandar, “Pengguna Internet Indonesia Peringkat ke-3 Terbanyak di Asia,” databoks.katadata.co.id, 2021.
C. Juditha, “Hoax Communication Interactivity in Social Media and Anticipation (Interaksi Komunikasi Hoax di Media Sosial serta Antisipasinya),” Pekommas, 2018
I. R. Cahyadi, “Survei KIC: Hampir 60% Orang Indonesia Terpapar Hoax Saat Mengakses Internet,” beritasatu.com, 2020.
Dimas Andhika Fikri, “3 Alasan Orang Suka Sebar Hoax soal Kesehatan : Okezone Lifestyle,” lifestyle.okezone.com, 2020.
P. Valdiviezo-Diaz, F. Ortega, E. Cobos, dan R. Lara-Cabrera, “A Collaborative Filtering Approach Based on Naïve Bayes Classifier,” IEEE Access, vol. 7, hal. 108581–108592, 2019, doi: 10.1109/access.2019.2933048
F.-J. Yang, “An Implementation of Naive Bayes Classifier,” 2018 Int. Conf. Comput. Sci. Comput. Intell., 2018, doi: 10.1109/csci46756.2018.00065
M. A. Rahmat, Indrabayu, dan I. S. Areni, “Hoax web detection for news in bahasa using support vector machine,” 2019 Int. Conf. Inf. Commun. Technol. ICOIACT 2019, hal. 332–336, Jul 2019, doi: 10.1109/ICOIACT46704.2019.8938425
S. Aphiwongsophon dan P. Chongstitvatana, “Detecting fake news with machine learning method,” ECTI-CON 2018 - 15th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol., hal. 528–531, Jan 2019, doi: 10.1109/ECTICON.2018.8620051
M. G. Hussain, M. R. Hasan, M. Rahman, J. Protim, dan S. Al Hasan, “Detection of Bangla Fake News using MNB and SVM Classifier,” Mei 2020
A. Y. Prayoga, A. I. Hadiana, dan F. R. Umbara, “Deteksi Hoax pada Berita Online Bahasa Inggris Menggunakan Bernoulli Naïve Bayes dengan Ekstraksi Fitur Tf-Idf,” J. Syntax Admiration, vol. 2, no. 10, hal. 1808–1823, 2021
N. Kousika, S. Deepa, C. Deephika, B. M. Dhatchaiyine, dan J. Amrutha, “A system for fake news detection by using supervised learning model for social media contents,” Proc. - 5th Int. Conf. Intell. Comput. Control Syst. ICICCS 2021, hal. 1042–1047, Mei 2021, doi: 10.1109/ICICCS51141.2021.9432096
Shivam Kohli, “Understanding a Classification Report For Your Machine Learning Model,” Medium, Nov-2019
A. Patle dan D. S. Chouhan, “SVM kernel functions for classification,” 2013 Int. Conf. Adv. Technol. Eng. ICATE 2013, 2013, doi: 10.1109/ICAdTE.2013.6524743
S. Wang dan C. D. Manning, “Baselines and bigrams: Simple, good sentiment and topic classification,” 50th Annu. Meet. Assoc. Comput. Linguist. ACL 2012 - Proc. Conf., vol. 2, no. July, hal. 90–94, 2012

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