Main Article Content
The COVID-19 pandemic is not over, the Coronavirus Disease 19 Pandemic due to the SARS-CoV-2 virus is spreading very quickly in almost every country in the world because of its human-to-human nature. The COVID-19 pandemic in Indonesia was detected in Depok, West Java on March 2, 2020. To deal with this, the government must decide on an efficient policy by observing the atmosphere and situation in each region. The way is through determining the risk status of COVID-19 transmission in an area in order to break the chain of transmission of COVID-19. In Indonesia, it is up to each local government to determine the risk status of Covid-19 transmission at the regional level. This has led to subjective evaluations by local leaders and introduced many unclear definitions and boundaries when determining the risk status of COVID-19 transmission. This is the reason behind this research, where the Tsukamoto Algorithm mathematical calculation form is based on the official variables and regulations in the area concerned in determining the risk status of COVID-19 transmission. The data used is the daily data for COVID-19 districts or cities in East Java. The data used are 38 district or city data groups consisting of 4 variables. The input variables are COVID-19 positive cases, Supek cases, and Probabe cases, and each variable is defined as 3 fuzzy sets, namely Low, Medium, and High. The output variables are defined in 4 fuzzy sets regarding the Risk Status for COVID-19 Transmission, such as the East Java Government regulations, namely the status of green, yellow, orange, and red. All variables use membership function of triangular curve representation. How to analyze data using the SPK-COVID application using the Codeigniter Framework. The success of the estimation form of the percentage of conformity status generated by comparing the results of the Tsukamoto Algorithm analysis with real COVID-19 transmission risk status data. After doing 4 repetitions of the analysis, in which each analysis tries to change the area in the fuzzy set, we get a structure with a percentage of status conformity that is usually 95.51%, on data from 38 districts or cities in East Java.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
D. Tuwu, “KEBIJAKAN PEMERINTAH DALAM PENANGANAN PANDEMI COVID-19,” Journal Publicuho, vol. 3, no. 2, 2020, doi: 10.35817/jpu.v3i2.12535.
C. Shen and Y. Bar-yam, “Color Zone Pandemic Response Version 2,” p. 2020, 2020, [Online]. Available: https://necsi.edu/color-zone-pandemic-response-version-2
Humaira, Y. Sonatha, C. Prabowo, H. Amnur, and R. Afyenni, “Comparative study of type-1 and type-2 fuzzy system in decision support system,” Indones. J. Electr. Eng. Informatics, vol. 6, no. 3, pp. 323–332, 2018, doi: 10.11591/ijeei.v6i3.391.
A. S. Mugirahayu, L. Linawati, and A. Setiawan, “Penentuan Status Kewaspadaan COVID-19 Pada Suatu Wilayah Menggunakan Metode Fuzzy Inference System (FIS) Mamdani,” Jurnal Sains dan Edukasi Sains, vol. 4, no. 1, 2021, doi: 10.24246/juses.v4i1p28-39.
C. Ardianto, H. Haryanto, and E. Mulyanto, “Prediksi Tingkat Kerawanan Kebakaran di Daerah Kudus Menggunakan Fuzzy Tsukamoto,” Creative Information Technology Journal, vol. 4, no. 3, 2018, doi: 10.24076/citec.2017v4i3.109.
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, 1965, doi: 10.1016/S0019-9958(65)90241-X.
BNPB, “Pengelompokan Kriteria Risiko COVID-19 di Daerah Berdasarkan Zonasi Warna,” 2021, [Online]. Available: https://bnpb.go.id/berita/pengelompokan-kriteria-risiko-covid19-di-daerah-berdasarkan-zonasi-warna.