KOMPARASI METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES DALAM KLASIFIKASI PELUANG PENYAKIT SERANGAN JANTUNG

Authors

  • Musthofa Galih Pradana Universitas Pembangunan Nasional Veteran Jakarta
  • Pujo Hari Saputro Universitas Sam Ratulangi
  • Dhina Puspasari Wijaya Universitas Alma Ata

DOI:

https://doi.org/10.21927/ijubi.v5i2.2659

Keywords:

Classification, Naïve Bayes, Support Vector Machine, Heart Disease

Abstract

The death rate in the world per year is 17.9 million due to cardiovascular disease, including heart and blood vessel disorders. This needs to be given more attention to anticipate the possible risk of a heart attack. One of the contributions in the field of technology to provide useful information about the risk of heart disease is by using a data processing approach or data mining technique by classifying the vulnerability to heart disease risk. The classification method used is Support Vector Machine and Naïve Bayes. The classification method will be carried out in a comparative process and the method that has the best accuracy will be sought. The scenarios used are 2 test scenarios, namely dividing the training data by 20% in scenario 1 and 40% in scenario 2. The final results of the research obtained are the best accuracy in the Support Vector Machine with scenario 1 of 87%.

References

W. H. Organization., “Cardiovascular Diseases.,†World Health Organization, 2020, p. 2020.

V. Kale, Enterptrise Performance Intelligence and Decision Patterns. CRC Press, 2018.

H. Tuhuteru and A. Iriani, “Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier,†J. Inform. J. Pengemb. IT, vol. 3, no. 3, pp. 394–401, 2018, doi: 10.30591/jpit.v3i3.977.

I. Riadi, R. Umar, and F. D. Aini, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),†Ilk. J. Ilm., vol. 11, no. 1, pp. 17–24, 2019, doi: 10.33096/ilkom.v11i1.361.17-24.

A. Supriyatna and W. P. Mustika, “Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil,†J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 2, no. 2, p. 152, 2018, doi: 10.30645/j-sakti.v2i2.78.

O. Arifin and T. B. Sasongko, “Analisa perbandingan tingkat performansi metode support vector machine dan naïve bayes classifier,†in Seminar Nasional Teknologi Informasi dan Multimedia 2018, 2018, vol. 6, no. 1, pp. 67–72.

H. T. Sueno, “Multi-class Document Classification using Support Vector Machine (SVM) Based on Improved Naïve Bayes Vectorization Technique,†Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3937–3944, 2020, doi: 10.30534/ijatcse/2020/216932020.

J. Gu and S. Lu, “An effective intrusion detection approach using SVM with naïve Bayes feature embedding,†Comput. Secur., vol. 103, p. 102158, 2021, doi: 10.1016/j.cose.2020.102158.

Y. Narayan, “Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification,†Mater. Today Proc., vol. 37, no. Part 2, pp. 3241–3245, 2020, doi: 10.1016/j.matpr.2020.09.093.

F. Fitriana, E. Utami, and H. Al Fatta, “Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes,†J. Komtika (Komputasi dan Inform., vol. 5, no. 1, pp. 19–25, 2021, doi: 10.31603/komtika.v5i1.5185.

D. A. Kristiyanti, A. H. Umam, M. Wahyudi, R. Amin, and L. Marlinda, “Comparison of SVM Naïve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter,†2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. Citsm 2018, pp. 1–6, 2019, doi: 10.1109/CITSM.2018.8674352.

S. Dey, S. Wasif, D. S. Tonmoy, S. Sultana, J. Sarkar, and M. Dey, “A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews,†2020 Int. Conf. Contemp. Comput. Appl. IC3A 2020, pp. 217–220, 2020, doi: 10.1109/IC3A48958.2020.233300.

S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm,†J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.

M. K. D. I. G. W. N. M. S. 2017 Dicky Nofriansyah, S.Kom., ALGORITMA DATA MINING DAN PENGUJIAN. DEEPUBLISH, 2017.

M. Kantardzic, Advances in Data Mining. 2019.

Downloads

Published

2022-12-31

Issue

Vol. 5 No. 2 (2022): Indonesian Journal of Business Intelligence (IJUBI)

Section

Articles

License

COPYRIGHT TRANSFER FORM

The copyright to this article is transferred to Alma Ata University Press if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to AAU Press. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.

We declare that:

1. This paper has not been published in the same form elsewhere.

2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.

3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.

Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to AAU Press. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.

The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

Retained Rights/Terms and Conditions

 

1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.

2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the AAU Press copyright notice are indicated, the copies are not used in any way that implies AAU Press endorsement of a product or service of any employer, and the copies themselves are not offered for sale.

3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.