Implementasi Algoritma C4.5 untuk Medeteksi Golongan Penderita Covid-19 Berdasarkan Gejala dan Penyebabnya
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Jul 1, 2023
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C4.5 Algorithm, Data Mining, Covid-19, ODP, PDP, OTG
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Abstract
Corona virus disease/Covid-19 is the name given by the World Health Organization (WHO) for sufferers infected with the 2019 novel corona virus. Seeing the development of the spread of the virus which continues to increase and the rate of its spread is even faster, it has created anxiety in all circles in public. Therefore, an appropriate method is needed that can make it easier to detect Covid-19 sufferers without having to do Covid-19 RT-PCR. So that the treatment of patients can be handled properly. The author uses the C4.5 Algorithm method which can predict whether the patient has Covid-19 based on the symptoms and causes. The results of this study researchers used the RapidMiner application to apply the C4.5 Algorithm method so that the results obtained showed an accuracy value of 70% for Covid-19 sufferers. So that with this research the public can self-detect and treat the symptoms of Covid-19 suffered if the patient belongs to the ODP group, the action that must be taken is to self-isolate for 14 days to carry out the recommended treatment and to avoid spreading the virus to the surrounding environment, so as well as other patient groups.
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Desi, E., & Aliyah, S. (2023). Implementasi Algoritma C4.5 untuk Medeteksi Golongan Penderita Covid-19 Berdasarkan Gejala dan Penyebabnya. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(3), 404–413. https://doi.org/10.35870/jtik.v7i3.872
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Computer & Communication Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Sugara, B., Widyatmoko, D., Prakoso, B.S. and Saputro, D.M., 2018. Penerapan Algoritma C4. 5 untuk Deteksi Dini Autisme Pada Anak. Semin. Nas. Teknol. Inf. dan Komun, 2018, pp.87-96.
Tabik, S., Gómez-Ríos, A., Martín-Rodríguez, J.L., Sevillano-García, I., Rey-Area, M., Charte, D., Guirado, E., Suárez, J.L., Luengo, J., Valero-González, M.A. and García-Villanova, P., 2020. COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images. IEEE journal of biomedical and health informatics, 24(12), pp.3595-3605. DOI: https://doi.org/10.1109/JBHI.2020.3037127.
Gurning, U.R., Rizaldi, S.T. and Syukron, H., 2022, July. Comparison of Naïve Bayes, C4. 5 and K-Nearest Neighbor for Covid-19 Data Classification. In 2022 International Symposium on Information Technology and Digital Innovation (ISITDI) (pp. 16-21). IEEE. DOI: https://doi.org/10.1109/ISITDI55734.2022.9944507.
Zaeni, I.A., Anzani, D.R.A., Putra, D.S., Devi, M., Hidayati, L. and Sudjono, I., 2020, September. Classifying the parental involvement on school from home during covid-19 using c4. 5 algorithm. In 2020 4th International Conference on Vocational Education and Training (ICOVET) (pp. 253-257). IEEE. DOI: https://doi.org/10.1109/ICOVET50258.2020.9230214.
Sancoko, S.D., Diwandari, S. and Fachrie, M., 2022, November. Ensemble Learning for Sentiment Analysis on Twitter Data Related to Covid-19 Preventions. In 2022 International Conference on Information Technology Research and Innovation (ICITRI) (pp. 89-94). IEEE. DOI: https://doi.org/10.1109/ICITRI56423.2022.9970234.
Febriantono, M.A., Prasetya, N.W. and Husna, N., 2022, October. Intelligent Ventilation System for COVID-19 Prevention using the IoT and Decision Tree. In 2022 8th International Conference on Education and Technology (ICET) (pp. 91-94). IEEE. DOI: https://doi.org/10.1109/ICET56879.2022.9990627.
Chen, P., 2021, June. Application of an Improved C4. 5 Algorithm in Shopping Websites. In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 63-66). IEEE. DOI: https://doi.org/10.1109/ICAICA52286.2021.9498086
Chen, P., 2021, July. The Application of an Improved C4. 5 Decision Tree. In 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC) (pp. 392-396). IEEE. DOI: https://doi.org/10.1109/ICNISC54316.2021.00078
Azwanti, N., 2018. Analisa Algoritma C4. 5 Untuk Memprediksi Penjualan Motor Pada Pt. Capella Dinamik Nusantara Cabang Muka Kuning. Inform. Mulawarman J. Ilm. Ilmu Komput, 13(1), p.33.
Mapa, J.S., Sison, A. and Medina, R.P., 2019, December. A Modified C4. 5 Classification Algorithm: With the Discretization Method in Calculating the Goodness Score Equivalent. In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/ICETAS48360.2019.9117309.
Oktavia, C.A., Rahmadwati, R. and Santoso, P.B., 2015. Analisis Kinerja Algoritma C4. 5 Pada Sistem Pendukung Keputusan Penentuan Jenis Pelatihan. EECCIS, 9(2), p.60419.
Pambudi, R.H., Setiawan, B.D. and Indriati, I., 2018. Penerapan Algoritma C4. 5 Untuk Memprediksi Nilai Kelulusan Siswa Sekolah Menengah Berdasarkan Faktor Eksternal. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(7), pp.2637-2643.
Nasrullah, A.H., 2018. Penerapan Metode C4. 5 untuk Klasifikasi Mahasiswa Berpotensi Drop Out. ILKOM Jurnal Ilmiah, 10(2), pp.244-250. DOI: https://doi.org/10.33096/ilkom.v10i2.300.244-250.
Rofiuddin, M., 2022. Dampak Corona Virus Disease 19 dan Obligasi Terhadap Nilai Tukar dan Sukuk di Indonesia. Jurnal Ilmiah Ekonomi Islam, 8(1), pp.1-9. DOI: http://dx.doi.org/10.29040/jiei.v8i1.4427.
Aditia, A., 2021. Covid-19: Epidemiologi, Virologi, Penularan, Gejala Klinis, Diagnosa, Tatalaksana, Faktor Risiko dan Pencegahan. Jurnal Penelitian Perawat Profesional, 3(4), pp.653-660.
Santoso, A.M.H., 2022. Covid-19: Varian Dan Mutasi. Jurnal Medika Hutama, 3(02 Januari), pp.1980-1986.
Aygün, I., Kaya, B. and Kaya, M., 2021. Aspect based twitter sentiment analysis on vaccination and vaccine types in covid-19 pandemic with deep learning. IEEE Journal of Biomedical and Health Informatics, 26(5), pp.2360-2369. DOI: https://doi.org/10.1109/JBHI.2021.3133103.
Pathak, Y., Shukla, P.K. and Arya, K.V., 2020. Deep bidirectional classification model for COVID-19 disease infected patients. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(4), pp.1234-1241. DOI: https://doi.org/10.1109/TCBB.2020.3009859.
Zhou, J., Jiang, X., Liu, C., Zhang, J., Liao, L. and Lu, J., 2021, December. Multi-Traffic Features Network Intrusion Detection Algorithm Based on C4. 5. In 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 548-552). IEEE. DOI: https://doi.org/10.1109/ICCWAMTIP53232.2021.9674129.
Ma, C., Zhang, J., Wang, L. and You, H., 2020, October. Network Attack Detection Based on Kernel Principal Component Analysis and Decision Tree. In 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (pp. 84-91). IEEE. DOI: https://doi.org/10.1109/CyberC49757.2020.00023.