ESTIMASI PARAMETER PADA MODEL MATEMATIKA PENYEBARAN COVID-19 DI TUBAN, JAWA TIMUR

Article Sidebar

PDF
Published: Oct 30, 2021
DOI: https://doi.org/10.26740/jram.v5n2.p132-143

Main Article Content

Ahmad Zaenal Arifin
Program Studi Matematika Universitas PGRI Ronggolawe
MUHAMMAD FAHRUR ROZI
Universitas Islam negeri Sunan Ampel
RIFA ATUL HASANAH
Unviersitas Islam Negeri Sunan Ampel
Dian Candra Rini Novitasari
Prodi Matematika Unviersitas Islam Negeri Sunan Ampel

Abstract


COVID-19 merupakan penyakit yang disebabkan oleh. Coronavirus 2 Syndrome Acute Syndrome (SARS-CoV-2). Penularan COVID-19 terjadi karena droplet yang dikeluarkan oleh orang yang terkena COVID-19. Penularan tersebut terjadi dengan cepat hingga meluas diberbagai daerah dipenjuru dunia. Salah satu daerah yang terdampak yaitu Kabupaten Tuban. Minimnya orang yang memperhatikan protokol kesehatan membuat penularan COVID-19 di Tuban semakin cepat. Berbagai tindakan yang tepat sangat dibutuhkan untuk mengatasi penyebaran COVID-19 di kota Tuban. Untuk memberikan tindakan yang tepat dibutuhkan sebuah analisa penyebaran penyakit dengan melihat beberapa parameter yang berpengaruh dalam penyebaran COVID-19. Pada penelitian ini dilakukan simualasi penyebaran COVID-19 menggunakan SEIR (Susceptible, Exposed, Infected, Recovered) dengan menggunakan metode Runge-Kutta. Populasi Exposed digunakan sebagai populasi yang dicurigai dapat terinfeksi COVID-19. Hasil dari penelitian ini menunjukan bahwa estimasi parameter pada model matematika dapat memperkecil error yang ditunjukkan dengan MAPE yang kecil.


Article Details

Issue
Vol. 5 No. 2 (2021): Oktober, JRAM
Section
Applied Mathematics
Creative Commons License

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

The copyright of the received article once accepted for publication shall be assigned to the journal as the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles.
 
 
Author Biographies

Ahmad Zaenal Arifin, Program Studi Matematika Universitas PGRI Ronggolawe

Program studi Matematika Universitas PGRI Ronggolawe Tuban

MUHAMMAD FAHRUR ROZI, Universitas Islam negeri Sunan Ampel

Prodi Matematika

RIFA ATUL HASANAH, Unviersitas Islam Negeri Sunan Ampel

Prodi Matematika Unviersitas Islam Negeri Sunan Ampel

Dian Candra Rini Novitasari, Prodi Matematika Unviersitas Islam Negeri Sunan Ampel

Prodi Matematika Unviersitas Islam Negeri Sunan Ampel

References

[1]      A. E. Gorbalenya et al., œSevere acute respiratory syndrome-related coronavirus: The species and its viruses a statement of the Coronavirus Study Group, bioRxiv, 2020.

[2]      B. Robson, œComputers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus, Comput. Biol. Med., no. February, p. 103670, 2020.

[3]      H. Lu, C. W. Stratton, and Y. Tang, œOutbreak of pneumonia of unknown etiology in Wuhan China: the mystery and the miracle, J. Med. Virol., 2020.

[4]      C. Sohrabi et al., œWorld Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19), International Journal of Surgery. 2020.

[5]      WHO, œNovel Coronavirus (COVID-19) Situation, WHO (June 11), 2020. .

[6]      Ihsanuddin, œFakta Lengkap Kasus Pertama Virus Corona di Indonesia, Kompas.com, 2020. .

[7]      BNPB, œStatistik Perkembangan COVID-19 Indonesia, Gugus Tugas Pecepatan Penanganan COVID-19 Republik Indonesia, 2020. .

[8]      Kementerian Kesehatan Republik Indonesia, Data dan Informasi Kesehatan Provinsi Jawa Timur. 2013.

[9]      P. K. Tuban, œKasus COVID-19 didominasi cluster pasar, Tuban, 2020.

[10]    A. Huppert and G. Katriel, œMathematical modelling and prediction in infectious disease epidemiology, Clin. Microbiol. Infect., vol. 19, no. 11, pp. 9991005, 2013.

[11]    A. Goyal, L. E. Liao, and A. S. Perelson, œWithin-host mathematical models of Hepatitis B virus infection: past, present, and future, Curr. Opin. Syst. Biol., vol. 18, pp. 2735, 2019.

[12]    Fatmawati, Windarto, and L. Hanif, œApplication of optimal control strategies to HIV-malaria co-infection dynamics, in Journal of Physics: Conference Series, 2018.

[13]    Y. Long and L. Wang, œGlobal dynamics of a delayed two-patch discrete SIR disease model, Commun. Nonlinear Sci. Numer. Simul., 2020.

[14]    [BPS] Badan Pusat Statistik, œBerita resmi statistik, Bps.Go.Id, 2019.

[15]    Q. Lin et al., œA conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action, Int. J. Infect. Dis., vol. 93, pp. 211216, 2020.

[16]    RI Kementrian Kesehatan, œMasalah Kesehatan Pada Lansia, Ditjen Yankes, 2018.

[17]    Worldometers, œIndonesia Population, Worldometers.Info. 2019.

[18]    P. K. Tuban, œInformasi tentang virus corona covid 19. 2020.

[19]    S. N. Qasem and S. M. Shamsuddin, œRadial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis, Appl. Soft Comput. J., 2011.

[20]    Z. Yong, Y. Li-juan, Z. Qian, and S. Xiao-yan, œMulti-objective optimization of building energy performance using a particle swarm optimizer with less control parameters, J. Build. Eng., vol. 32, p. 101505, 2020.

[21]    L. Cagnina, œA particle swarm optimizer for multi-objective optimization, J. Comput. ¦, 2005.

[22]    G. Ahn and S. Hur, œEfficient genetic algorithm for feature selection for early time series classification, Comput. Ind. Eng., vol. 142, no. February, p. 106345, 2020.

[23]    H. Harkat, A. E. Ruano, M. G. Ruano, and S. D. Bennani, œGPR target detection using a neural network classifier designed by a multi-objective genetic algorithm, Appl. Soft Comput. J., vol. 79, pp. 310325, 2019.

[24]    P. Wang, X. Yan, and F. Zhao, œMulti-objective optimization of control parameters for a pressurized water reactor pressurizer using a genetic algorithm, Ann. Nucl. Energy, vol. 124, pp. 920, 2019.

[25]    H. Wang et al., œA hybrid multi-objective firefly algorithm for big data optimization, Appl. Soft Comput. J., 2018.

[26]    M. Singh, R. N. Patel, and D. D. Neema, œRobust tuning of excitation controller for stability enhancement using multi-objective metaheuristic Firefly algorithm, Swarm Evol. Comput., 2019.

[27]    L. Lv, J. Zhao, J. Wang, and T. Fan, œMulti-objective firefly algorithm based on compensation factor and elite learning, Futur. Gener. Comput. Syst., 2019.

.