Penerapan Seleksi Fitur pada Deteksi Coronavirus Disease 19 (COVID-19) berbasis Random Forest
DOI:
https://doi.org/10.32493/jtsi.v7i4.40755Keywords:
Feature Selection; Prediction; COVID-19Abstract
Data mining using machine learning algorithms can be used to help analyze historical data to predict COVID-19. The dataset used for predicting COVID-19 has many features, but those features have the possibility of redundancy or irrelevance that can cause a decrease in classifier performance. This research proposes a model that implements feature selection to select relevant features and can provide improved performance predictions for diagnose COVID-19. Some proposed feature selection techniques are Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Forward Floating Selection ( SBFS), Sequential Backward Floating Selection (SBFS), and selectKBest. The classification algorithm used to classify is Random Forest. The model that gives the best performance value is the model that applies the SFS dan SFFS as feature selection.
References
Burhan, E., Susanto, A. D., Nasution, S. A., Ginanjar, E., Pitoyo, W., Susilo, A., & Dkk. (2020). Pedoman Tatalaksana COVID-19. In Pedoman Tatalaksana COVID-19 (3rd ed.).
Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., & Napoli, R. Di. (2022). Features, Evaluation, and Treatment of Coronavirus (COVID-19). StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan–., December 2020, 1–49.
Cennimo, D. J. (2024). Coronavirus Disease 2019 (COVID-19) (pp. 1–4). Medscape.
Gorunescu, F. (2011). Data mining: concepts and techniques. In Chemistry & https://doi.org/10.1007/978-3-642-19721-5
Guo, Y.-R., Cao, Q.-D., Hong, Z.-S., Tan, Y.-Y., Chen, S.-D., Jin, H.-J., Tan, K.-S., Wang, D.-Y., & Yan, Y. (2020). The Origin, Transmission and Clinical Therapies on Coronavirus Disease 2019 (COVID-19) Outbreak – An Update on the Status. Military Medical Research, 7(11), 1–10. https://doi.org/10.1186/s40779-020-00240-0
Jiawei, H., Kamber, M., Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In San Francisco, CA, itd: Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-381479-1.00001-0
Liu, H., Jiang, H., & Zheng, R. (2016). The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification. Computational and Mathematical Methods in Medicine, 2016. https://doi.org/10.1155/2016/7369137
Mendez, C. M. (2020). Solidarity” Clinical Trial for COVID -19 Treatments. Brazilian Journal of Impleantology and Health Sciences, 1–6.
Nair, R., & Bhagat, A. (2019). Feature selection method to improve the accuracy of classification algorithm. International Journal of Innovative Technology and Exploring Engineering, 8(6), 124–127.
Oxford University. (2022). The RECOVERY Trial - two years on (pp. 1–2). Oxford University.
Sahin, A. R., Erdogan, A., Mutlu Agaoglu, P., Dineri, Y., Cakirci, A. Y., Senel, M. E., Okyay, R. A., & Tasdogan, A. M. (2020). 2019 Novel Coronavirus (COVID-19) Outbreak: A Review of the Current Literature. Eurasian Journal of Medicine and Oncology, 4(1), 1–7. https://doi.org/10.14744/ejmo.2020.12220
Sindi, S., Ningse, W. R. O., Sihombing, I. A., Ilmi R.H.Zer, F., & Hartama, D. (2020). Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia. Jti, 4(1), 166–173.
World Health Organization. (2020). Laboratory Testing for 2019 Novel Coronavirus (2019-nCoV) in Suspected Human Cases. In WHO - Interim guidance (pp. 1–7). World Health Organization. https://www.who.int/publications/i/item/WHO-2019-nCoV-lab-testing-2021.1-eng
Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2016). A Survey on Evolutionary Computation Approaches to Feature Selection. IEEE Transactions on Evolutionary Computation, 20(4), 606–626. https://doi.org/10.1109/TEVC.2015.2504420
Yanti, E., Fridalni, N., & Harmawati. (2020). Mencegah Penularan Virus Corona. Journal Abdimas Saintika, 2, 7. https://jurnal.syedzasaintika.ac.id/index.php/abdimas/article/view/553/pdf
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