Klasifikasi Kebijakan Level Ppkm Berdasarkan Data Penyebaran Covid-19 Dengan Pendekatan Fuzzy Rough Set

Murni Marbun(1*), S Sulindawaty(2), Agnes P Barus(3), Kezia Dewi C H Hutajulu(4),


(1) STMIK Pelita Nusantara
(2) STMIK Pelita Nusantara
(3) STMIK Pelita Nusantara
(4) STMIK Pelita Nusantara
(*) Corresponding Author

Abstract


The government's policy in suppressing the transmission of COVID-19 is to limit people's mobility. The policy called Pemberlakukan Pembatasan Kegiatan Masyarakat (PPKM) with different levels for each district or city. The COVID-19 pandemic has not ended so far, so we need a method that can assist government in suppressing the spread of COVID-19 by classifying PPKM level policies in districts or cities that are adjusted according to the assessment level of each districts or cities. This study aims to classify PPKM levels with the fuzzy rough set method approach. The determination of the PPKM level policy based on epidemiological parameters, namely the number of positive cases of Covid-19 for 100,000 population, number of cases treated for 100,000 population and number of deaths for 100,000 population. The study began by inputting the COVID-19 spread dataset, pre-processing the data, and classified the data using the fuzzy rough set method. The stages of the fuzzy rough set method began with transforming the data into fuzzy form in the form of a table called an information system and partitioning the set of objects based on the similarity of the attributes contained in the decision system table into disjoint subsets according to 3 decision class labels, namely level 1, level 2 and level 3. The output rules consist of certain rules where the degree of fuzzy membership in the fuzzy equivalence class is the future effectiveness for each certain rules, and possible rules. Classification with fuzzy rough set approach obtains output of 58 rules consisting of 6 certain rules and 52 possible rules. The classification results show that PPKM level policies can be classified using the fuzzy rough set approach

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References


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DOI: http://dx.doi.org/10.30645/j-sakti.v6i1.458

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