DIAGNOSIS OF CORONAVIRUS DISEASE 2019 (COVID-19) SURVEILLANCE USING C4.5 ALGORITHM
Diagnosis Penyakit Coronavirus 2019 (COVID-19) Surveillance Menggunakan Algoritma C4.5
Coronavirus Disease 2019 (COVID-19) has become a pandemic in Indonesia as a non-natural disaster in the form of disease outbreaks which must be undertaken as a response. The Ministry of Health in the Republic of Indonesia published a guidebook for prevention and control of COVID-19 in its response efforts. This guideline is intended for health officials as a reference in preparing for COVID-19. This handbook contains early detection and response activities to identify conditions of PDP, ODP, OTG, or confirmed cases of COVID-19. The efforts made are adjusted to the world situation progress from COVID-19 which is monitored by the World Health Organization (WHO). From the results of documentation studies that have been carried out on the COVID-19 pandemic in Indonesia, there are several problems that must be resolved from the prevention of the disease outbreak COVID-19. Lack of knowledge and awareness of the general public in the prevention and control of COVID-19 is one of the factors increasing the spread of that virus in Indonesia. Furthermore, there are difficulties in carrying out surveillance, early detection, contact tracing, infection prevention or control, and risk communication or people empowerment. This is due to the lack of implementation and testing on artificial intelligence methods for COVID-19 diagnosis that can be used by the public. The purpose of this research is to make a diagnosis of surveillance classification which includes PDP, ODP, and OTG using the C4.5 algorithm. The results showed that the diagnosis of the COVID-19 surveillance category using the C4.5 algorithm was successfully modeled into a decision tree with PDP, ODP, and OTG classification. The testing process in a confusion matrix with 3 (three) classes produces an accuracy rate of 92.86% which is included in the excellent classification category.
Ali, M., Son, D.-H., Kang, S.-H., & Nam, S.-R. (2017). An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy. Energies, 10, 1830. https://doi.org/10.3390/en10111830
Amrin, A., Satriadi, I., & Rosanto, O. (2019). ALGORITMA C4. 5 UNTUK DIAGNOSA PENYAKIT TUBERKULOSIS. Jurnal Khatulistiwa Informatika, 7(2).
Anggito, A., & Setiawan, J. (2018). Metodologi penelitian kualitatif. CV Jejak (Jejak Publisher).
Anwar, N., Pranolo, A., & Kurnaiwan, R. (2018). Grouping the community health center patients based on the disease characteristics using C4. 5 decision tree. IOP Conference Series: Materials Science and Engineering, 403(1), 12084.
Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education. Cengage Learning.
Bairagi, V., & Munot, M. V. (2019). Research methodology: A practical and scientific approach. CRC Press.
Buulolo, E. (2020). Data Mining Untuk Perguruan Tinggi. Deepublish.
Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi-experimental designs for research. Ravenio Books.
Castaño, A. P. (2018). Practical Artificial Intelligence: Machine Learning, Bots, and Agent Solutions Using C. Apress.
Efron, S. E., & Ravid, R. (2018). Writing the literature review: A Practical Guide. Guilford Publications.
Kemenkes RI. (2020). Pedoman Pencegahan dan Pengendalian Coronavirus Disease (COVID-19) (L. Aziza, A. Aqmarina, & M. Ihsan (eds.); Revisi Ke4). Kementerian Kesehatan RI; Direktorat Jenderal Pencegahan dan Pengendalian Penyakit (P2P). https://infeksiemerging.kemkes.go.id/
Lazar, J., Feng, J. H., & Hochheiser, H. (2017). Research methods in human-computer interaction. Morgan Kaufmann.
Li, F., Zhang, L., & Zhang, Z. (2017). Dynamic Fuzzy Machine Learning. Walter de Gruyter GmbH & Co KG.
Menkes RI. (2020). Surat Edaran No. HK.02.01/MENKES/2020 tentang Protokol Isolasi Diri Sendiri dalam Penanganan Coronavirus Diseases (COVID-19). Kementerian Kesehatan RI. https://covid19.kemkes.go.id/
Muharto & Ambarita, A. (2016). Metode Penelitian Sistem Informasi: Mengatasi Kesulitan Mahasiswa Dalam Menyusun Proposal Penelitian. Deepublish. Yogyakarta.
Mujahidin, A., & Pribadi, D. (2017). Penerapan Algoritma C4. 5 Untuk Diagnosa Penyakit Pneumonia Pada Anak Balita Berbasis Mobile. Jurnal Swabumi, 5(2), 155–161.
Mulyani, S. (2017). Metode Analisis dan Perancangan Sistem. Abdi Sistematika.
PDPI. (2020). Pnemonia Covid-19. Diagnosis & Penatalaksanaan di Indonesia. https://www.klikpdpi.com/
Rafiska, R., Defit, S., & Nurcahyo, G. W. (2018). Analisis Rekam Medis untuk Menentukan Pola Kelompok Penyakit Menggunakan Algoritma C4. 5. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 2(1), 391–396.
RapidMiner GmbH. (2014). RapidMiner Studio: Manual. RapidMiner GmbH. https://doi.org/http://docs.rapidminer.com/downloads/RapidMiner-v6-user-manual.pdf
RapidMiner GmbH. (2019). RapidMiner 9: Operator Reference Manual. RapidMiner GmbH. https://docs.rapidminer.com/latest/studio/operators/rapidminer-studio-operator-reference.pdf
Santoso, B., & Azis, A. I. S. (2020). Machine Learning & Reasoning Fuzzy Logic Algoritma, Manual, Matlab, & Rapid Miner. Deepublish.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
Tarigan, D. M., Rini, D. P., & Puspita, V. (2017). Perancangan Data Mining untuk Klasifikasi Prediksi Penyakit ISPA dengan Algoritma C4. 5. Annual Research Seminar (ARS), 3(1), 179–182.
Wiguna, W. (2020). Decision Tree of Coronavirus Disease (COVID-19) Surveillance. IEEE Dataport. https://doi.org/10.21227/remc-6d63
Wiguna, W., & Riana, D. (2020). Laporan Penelitian Dosen Yayasan: Diagnosis of Coronavirus Disease 2019 (COVID-19) Surveillance Using C4.5 Algorithm. Universitas Adhirajasa Reswara Sanjaya. https://doi.org/10.21227/9anj-0p64
Abstract viewed = 1545 times
PDF downloaded = 1028 times
Copyright (c) 2020 Wildan Wiguna, Dwiza Riana
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to the PILAR Nusa Mandiri journal as the publisher of the journal, and the author also holds the copyright without restriction.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases, and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with written permission from the PILAR Nusa Mandiri journal.
PILAR Nusa Mandiri journal, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal. In any way, the contents of the articles and advertisements published in the PILAR Nusa Mandiri journal are the sole and exclusive responsibility of their respective authors and advertisers.