Published: 2021-09-20
Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor
DOI: 10.35870/jtik.v5i3.221
Abstract
The Covid -19 virus spread in the world, especially in Indonesia, very fast. This epidemic is of concern around the world because it has a quite bad impact in various sectors. With existing technological advances, the Expert System can assist medical personnel in detecting the Covid -19 Virus. The purpose of the author in conducting the study was to detect the Covid-19 virus as easily as possible with symptom data obtained from patients who had consultations. The Naïve Bayes method is a method that uses probability and statistics that can predict a person's chance of being exposed to Covid-19 in the future based on symptoms experienced in the previous period packed with a web-based program. For comparison, the author uses the Certainty Factor Method. Certainty Factor is a method that aims to determine the certainty value which is based on the previous calculation of CF value by manual calculation. The Naïve Bayes method can group the symptoms obtained from the official WHO website which has been given an indicator of the percentage of someone exposed to the Covid-19 Virus based on the symptom data experienced to determine a person exposed to the Covid-19 Virus. While the Certainty Factor method gets the confidence of someone exposed to the symptoms of the Covid-19 virus by using the calculation indicator on the CF value that has been consulted by the user, which can provide a percentage level of confidence that is 86%.
Keywords
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 3 (2021)
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Section: Computer & Communication Science
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Published: 2021-09-20
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License: CC BY 4.0
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Copyright: © 2021 Authors
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DOI: 10.35870/jtik.v5i3.221
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No author biographies available.
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Suryana, M.F., Fauziah, F. and Sari, R.T.K., 2020. Implementasi Sistem Pakar Menggunakan Metode Certainty Factor Untuk Mendiagnosa Dini Corona Virus Desease (COVID-19). Jurnal Media Informatika Budidarma, 4(3), pp.559-566.
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Sinuraya, I., Triayudi, A. and Sholihati, I.D., 2020. Covid-19 Diagnosis Based Android Mobile Application using Certainty Factor Method. SISFOTENIKA, 10(2), pp.203-213.
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Insani, M.I., Alamsyah, A. and Putra, A.T., 2018. Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods. Sci. J. Informatics, 5(2), pp.185-193.
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Purnomo, A., Hartono, R. and Munawaroh, H., 2018. Naïve Bayes Approach for Expert System Design of Children Skin Identification Based on Android. MS&E, 333(1), p.012105.
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Muqorobin, M., Utomo, P.B., Nafi’Uddin, M. and Kusrini, K., 2019. Implementasi Metode Certainty Factor pada Sistem Pakar Diagnosa Penyakit Ayam Berbasis Android. Creative Information Technology Journal, 5(3), pp.185-195.
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Dewi, I.C., Soebroto, A.A. and Furqon, M.T., 2015. Sistem Pakar Diagnosa Penyakit Sapi Potong Dengan Metode Naive Bayes. Journal of Environmental Engineering and Sustainable Technology, 2(2), pp.72-78.
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Santoso, P.H., Fauziah, F. and Nurhayati, N., 2020. Application of Data Mining Classification for Covid-19 Infected Status Using Algortima Naïve Method. Jurnal Mantik, 4(1, May), pp.267-275.
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Setiawan, W. and Ratnasari, S., 2014. Sistem Pakar Diagnosis Penyakit Mata Menggunakan Naïvebayes Classifier. Prosiding Semnastek, 1(1).
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