Penerapan Stacking untuk Optimasi Model Diagnosa Coronavirus Disease 19 (COVID-19)

Penulis

DOI:

https://doi.org/10.32493/jtsi.v7i2.38936

Kata Kunci:

Stacking, Optimasi Model Diagnosa, COVID-19

Abstrak

Hasil pemeriksaan laboratorium pada pasien COVID-19 tidak spesifik, tetapi sering ditemukan limfopenia, peningkatan laktat dehidrogenase, dan peningkatan aminotransferase. Sedangkan pemeriksaan pencitraan toraks dapat menunjukkan gambaran pneumonia. Sampai saat ini, belum terdapat terapi spesifik dalam penanganan COVID-19. Terdapat dua studi terbesar tentang terapi COVID-19 yang hingga saat ini masih berjalan secara global. Studi menunjukkan bahwa antivirus favipiravir, remdesivir, dan tocilizumab mungkin memiliki beberapa manfaat untuk penanganan COVID-19, dan sudah diperbolehkan penggunaannya di Indonesia. Telah banyak metode diagnosa menggunakan machine learning yang digunakan untuk mendeteksi seseorang terkena COVID-19 atau tidak. Namun, keakuratan tes dapat bervariasi tergantung pada saat sampel Anda diambil selama perjalanan penyakit Anda. Jika Anda dites terlalu cepat setelah terpapar COVID-19, mungkin tidak ada cukup virus di tubuh Anda untuk mendapatkan hasil yang akurat. Jika ini masalahnya pada saat tes, tes Anda mungkin kembali negatif, bahkan jika Anda benar-benar memiliki virus. Ini akan dianggap sebagai tes 'negatif palsu'. Penting untuk dipahami bahwa profesional perawatan kesehatan mempertimbangkan sejumlah faktor dalam membuat diagnosis COVID-19. Pada penelitian ini menggunakan metode eksperimen dengan membuat aplikasi untuk menerapkan algoritma yang diusulkan. Kemudian menguji model menggunakan dataset sekunder yang diunduh dari Kaggle dan mengukur kinerja model.

Biografi Penulis

Yulianti Yulianti, Universitas Pamulang

Received S.Kom. (Bachelor Degree) in Informatics Engineering from Pamulang University, Tangerang Selatan, Banten, and M.Kom (Master Degree) in Informatics Engineering from STMIK ERESHA, Jakarta. She is a lecturer at Informatics Engineering, Pamulang University. Her current research interests include Information System, software engineering, and intelligent systems.

Publication:

SCOPUS ID: 57216504383
SINTA ID: 6007805

Google Scholar: NqJihzUAAAAJ

 

Referensi

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.

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

McIntosh, K., Hirsch, M. S., & Bloom, A. (2020). Coronavirus Disease 2019 (COVID-19). UpToDate, 1–27. https://www.cmim.org/PDF_covid/Coronavirus_disease2019_COVID-19_UpToDate2.pdf

Mendez, C. M. (2020). Solidarity” Clinical Trial for COVID -19 Treatments. Brazilian Journal of Impleantology and Health Sciences, 1–6.

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

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

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 2 April 2024

Unduhan

Diterbitkan

2024-04-30

Cara Mengutip

Yulianti, Y., Mulyati, S., & Desyani, T. (2024). Penerapan Stacking untuk Optimasi Model Diagnosa Coronavirus Disease 19 (COVID-19). Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(2), 579–587. https://doi.org/10.32493/jtsi.v7i2.38936

Terbitan

Vol 7 No 2 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Yulianti Yulianti, Sri Mulyati, Teti Desyani

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