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.

Fulltext View|Download

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

Article Metrics:

Article Info

Section: Research Article

Language : ID

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

Recent articles

Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia Kriptostegano Menggunakan Data Encryption Standard dan Least Significant Bit dalam Pengamanan Pesan Gambar Analisis Kepuasan Pengguna Sistem Informasi Akademik Universitas Teknologi Sumbawa dengan Pendekatan Overview Analitik More recent articles

Most cited articles

Studi Komparasi Ekstraksi Fitur pada Pengenalan Wajah Menggunakan Principal Component Analysis (PCA) dan Wavelet Daubechies PENYELESAIAN MASALAH JOB SHOP MENGGUNAKAN ALGORITMA GENETIKA KLASIFIKASI UCAPAN KATA DENGAN SUPPORT VECTOR MACHINE Enterprise Architecture Model untuk Aplikasi Government OPTICAL CHARACTER RECOGNITION MENGGUNAKAN ALGORITMA TEMPLATE MATCHING CORRELATION More cited articles

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

Last update:

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning
Ahmad Zamsuri, Sarjon Defit, Gunadi Widi Nurcahyo. Journal of Advances in Information Technology, 15 (4), 2024. doi: 10.12720/jait.15.4.519-531
Sentiment Analysis to Assess Customer Retention on Instagram Social Media Using Naïve Bayes Classifier and Support Vector Machine
Fandi Rahmat Halim, Rice Novita, Mustakim, M Afdal. 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2024. doi: 10.1109/eSmarTA62850.2024.10638885
Classification of Hoax News Using Machine Learning and Neural Networks with BERT Embeddings
Budi Juarto. 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2023. doi: 10.1109/ICE3IS59323.2023.10335413

Last update: 2024-11-22 11:07:47

No citation recorded.

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

The authors who submit the manuscript must understand that the article's copyright belongs to the author(s) if accepted for publication. However, the author(s) grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors should also understand that their article (and any additional files, including data sets, and analysis/computation data) will become publicly available once published under that license. See our copyright policy. By submitting the manuscript to Jmasif, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:

their article is original, written by the mentioned author(s),
has never been published before,
does not contain statements that violate the law, and
does not violate the rights of others, is subject to copyright held exclusively by the author(s), is free from the rights of third parties, and the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

Copyright and other proprietary rights related to the article, such as patents,
The right to use the substance of the article in its own future works, including lectures and books,
The right to reproduce the article for its own purposes,
The right to archive all versions of the article in any repository, and
The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (Jurnal Masyarakat Informatika).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. Jmasif will not be held responsible for anything arising because of the writer's internal dispute. Jmasif will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. Jmasif allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and Jmasif to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.