Implementasi CBR-AHP Penentuan Rawat Inap Pasien Covid-19 Rumah Sakit dengan Sumberdaya Terbatas
Keywords:
CBR-AHP, Covid-19, Expert System, Nei&LiAbstract
Covid-19 is one of the most dangerous and number one killer viruses in the world today and cannot be handled properly. Covid-19 patients with mild symptoms do not require hospitalization unless there are concerns about the possibility of a rapid worsening and according to medical considerations. Patients who are elderly and have comorbid diseases have a greater risk of experiencing more severe symptoms and experiencing death, so they can be considered for treatment. To make it easier to determine inpatients for COVID-19 patients, an expert system with the CBR-AHP method is needed. This system can be used to conduct consultations on Covid-19 disease and provide solutions for the treatment of the type of Covid-19 disease found where the consultation results are obtained from the highest Nei&Li similarity value. Of all the variants found above, the highest Nei&Li similarity value is Omicron with a similarity of 1,000. The system for determining the inpatient status of Covid-19 patients in hospitals with the Nei&Li algorithm will recommend Covid-19 diseases found with similarity above 0.5 and similarity below 0.5 will be entered into the review table to find a solution.References
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.
Menkes, Pedoman Pencegahan Dan Pengendalian Coronavirus Disease 2019 (Covid-19), Jakarta, 2020.
S. Kusumadewi, Artificial Intellegence, Yogyakarta: Graha Ilmu, 2015.
E. Rich, Artifical Intelligence, Singapore: McGraw-Hill Inc, 1991.
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.
S. Wibisono, W. Hadikurniawati, H. Februariyanti and M. Utomo, "An Improvement Of Similarity In Case Based Reasoning Using Subjective-Generalized Weight," Journal of Theoretical and Applied Information Technology, vol. XCVIII, no. 5, pp. 864-875, 2020.
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.
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.
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.
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.
A. Pahlawan and S. Wibisono, "Implementasi Case Based Reasoning Untuk Sistem Diagnosis Hama Dan Penyakit Tanaman Cabe Merah Menggunakan Algoritma Similaritas Neyman," Prosiding SINTAK, pp. 155-162, 2017.
