Metode Kernel Distance Classifier Terhadap Klasifikasi Penyakit Jantung
Article Sidebar
Main Article Content
Abstract
This study compares the Support Vector Machine (SVM) and Kernel Distance Classification (KDC) methods to classify heart disease. SVM works by transforming data into higher dimensions using the kernel and classifying data linearly using a hyperplane. Meanwhile, KDC works by finding points that represent each classification from the data that has been transformed into a higher dimension using the kernel, and the new data is predicted based on the closest distance from the point of each classification. The results show that the accuracy produced by SVM is 81.11%. The accuracy produced by the SVM model is better than that produced by the KDC model of 80.47% with a difference of 0.64%, even though both models use kernel transformation.
Downloads
Article Details
Please find the rights and licenses in the Journal of Information Technology and Computer Engineering (JITCE).
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)’ Warranties
The author(s) warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary permissions to quote from other sources have been obtained by the author(s).
3. User Rights
JITCE adopts the spirit of open access and open science, which disseminates articles published as free as possible under the Creative Commons license. JITCE permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and JITCE on distributing works in the journal.
4. Rights of Authors
Authors retain the following rights:
- Copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in future own works, including lectures and books,
- the right to reproduce the article for own purposes,
- the right to self-archive the article.
- the right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Journal of Information Technology and Computer Engineering).
5. Co-Authorship
If the article was jointly prepared by other authors; upon submitting the article, the author is agreed on this form and warrants that he/she has been authorized by all co-authors on their behalf, and agrees to inform his/her co-authors. JITCE will be freed on any disputes that will occur regarding this issue.
7. Royalties
By submitting the articles, the authors agreed that no fees are payable from JITCE.
8. Miscellaneous
JITCE will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed and JITCE or its sublicensee has become obligated to have the article published. JITCE may adjust the article to a style of punctuation, spelling, capitalization, referencing and usage that it deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers.
References
[2] “Press Release, World Heart Day PERKI 2019 - News & Event | Perhimpunan Dokter Spesialis Kardiovaskuler Indonesia (PERKI).” http://www.inaheart.org/news_and_events/news/2019/9/26/press_release_world_heart_day_perki_2019 (accessed Nov. 29, 2020).
[3] “Kementerian Kesehatan Republik Indonesia.” https://www.kemkes.go.id/article/view/17073100005/penyakit-jantung-penyebab-kematian-tertinggi-kemenkes-ingatkan-cerdik-.html (accessed Nov. 29, 2020).
[4] P. Tabesh, G. Lim, S. Khator, and C. Dacso, “A support vector machine approach for predicting heart conditions,” IIE Annu. Conf. Expo 2010 Proc., 2010.
[5] Y. a Sandhy, “Prediction of Heart Diseases using Support Vector Machine,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 8, no. 2, pp. 126–135, 2020, doi: 10.22214/ijraset.2020.2021.
[6] A. Rodan, H. Faris, J. Alsakran, and O. Al-Kadi, “A Support Vector Machine approach for churn prediction in telecom industry,” Inf., vol. 17, no. 8, pp. 3961–3970, 2014.
[7] D. Chen, Q. He, and X. Wang, “On linear separability of data sets in feature space,” Neurocomputing, vol. 70, no. 13–15, pp. 2441–2448, 2007, doi: 10.1016/j.neucom.2006.12.002.
[8] “UCI Machine Learning Repository: Heart Disease Data Set.” https://archive.ics.uci.edu/ml/datasets/heart+disease (accessed Nov. 29, 2020).
[9] Q. S. Xu and Y. Z. Liang, “Monte Carlo cross validation,” Chemom. Intell. Lab. Syst., vol. 56, no. 1, pp. 1–11, 2001, doi: 10.1016/S0169-7439(00)00122-2.
[10] Z. Kurucova, J. Medová, and A. Tirpakova, “Cogent Education The effect of different online education modes on the English language learning of media studies students The effect of different online education modes on the English language learning of media studies students,” Cogent Arts Humanit., 2017, doi: 10.1080/2331186X.2018.1523514.