Deteksi Dini Varian Covid-19 Dengan Metode CBR-AHP Dan Sorgenfrei

Mohamad Riza  Darmawan; Setyawan  Wibisono

doi:10.51903/pixel.v15i1.747

Authors

Mohamad Riza Darmawan Unisbank Semarang
Setyawan Wibisono Unisbank Semarang

DOI:

https://doi.org/10.51903/pixel.v15i1.747

Keywords:

CBR-AHP, Covid-19, Sorgenfrei

Abstract

The case fatality rate caused by Covid-19 in Indonesia is around 2.9%. The death rate by age group is from all Covid-19 patients who died, including 0.5% aged 0-5 years, 0.5% aged 6-18 years, 2.8% aged 19-30 years, 12.7 % were aged 31–45 years, 36.8% were aged 46–59 years, and 46.7% were aged 60 years and over. With the large number of cases that occurred in Indonesia, it turned out to be due to the lack of proper handling of the early symptoms of the Covid-19 virus. To overcome this, it is needed as a tool for early detection of the Covid-19 variant by using the CBR-AHP expert system with the Sorgenfrei algorithm. The results of the consultation for the early detection of the Covid-19 variant were obtained from the highest similarity value of Sorgenfrei. Of all the variants found above, the highest similarity value of Sorgenfrei is Omicron with a similarity of 1,000. Omicron has the highest value because it has a value of a, which is the same symptom between old cases and large new cases.

References

[1] F. Setiawan and S. Wibisono, "Algoritma Bray&Curtis Berbobot Pada Cbr Penentuan Keluarga Terdampak Covid-19," Jurnal Manajemen informatika & Sistem Informasi, vol. IV, no. 2, pp. 130-139, 2021.
[2] S. Kusumadewi, Artificial Intellegence, Yogyakarta: Graha Ilmu, 2015.
[3] E. Rich, Artifical Intelligence, Singapore: McGraw-Hill Inc, 1991.
[4] Amriana, D. Nugraha and Rahmatanti, "Sistem Pakar Diagnosa Penyakit Lambung Menggunakan Metode Case Based Reasoning Berbasis Web," (Journal of Computer Engineering System and Science, vol. V, no. 1, pp. 114-123, 2020.
[5] A. Amanaturohim and S. Wibisono, "Penentuan Parameter Terbobot Menggunakan Pairwise Comparison Untuk CBR Deteksi Dini Penyakit Mata," Jurnal Sains Komputer & Informatika, vol. V, no. 1, pp. 280-294, 2021.
[6] K. Iman and S. Wibisono, "Pembobotan Menggunakan Pairwise Comparison Pada Case Based Reasoning Rekomendasi Hotel," Jurnal Manajemen informatika & Sistem Informasi, vol. IV, no. 1, pp. 9-18, 2021.
[7] N. K. Umami and W. Setyawan, "Deteksi Dini Penyakit Balita Menggunakan Algoritma Sorensen Berbobot," Jurnal Ilmiah informatika, vol. IX, no. 2, pp. 60-67, 2021.
[8] N. Fitrianto and S. Wibisono, "Sistem Pakar Penanganan Gangguan Layanan Indihome Pada Pelanggan PT Telkom Indonesia Menggunakan Metode Case-Based Reasoning Dengan Algoritma Similaritas Jaccard," Prosiding SINTAK, Vols. Fitrianto, N., Wibisono, S., (2018) , 2018, pp., pp. 472-479, 2018.
[9] S. B. Sakur, PHP5 Pemograman berorientasi objek Konsep & Implementas, Yogyakarta: Andi, 2016.
[10] B. Nugroho, Database Relasional Dengan MySQL, Yogyakarta: Andi, 2015.
[11] A. Aamodt and E. Plaza, "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches," IOS Press, vol. VII, no. 1, pp. 39-59, 1994.
[12] T. Saaty, "Decision Making With Analytical Hierarchy Process," International journal service science, vol. I, no. 1, pp. 83-98, 2008.
[13] S. Cha, "Comprehensive survey on distance/similarity measures between probability density functions," INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES, vol. I, no. 4, pp. 300-307, 2007.
[14] S. Choi, S. Cha and C. Tappert, "A Survey of Binary Similarity and Distance Measures," SYSTEMICS, CYBERNETICS AND INFORMATICS, vol. VIII, no. I, pp. 43-48, 2010.