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


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

Full Text:



WHO, “COVID-19 weekly epidemiological update,” World Heal. Organ., no. 58, pp. 1–23, 2021.

Menko Perekonomian, “SIARAN PERS HM.4.6/187/SET.M.EKON.3/07/2021 Penerapan PPKM untuk Mengendalikan Laju Covid-19 dan Menjaga Kehidupan Masyarakat,” pp. 20–22, 2021.

Menteri Dalam Negeri Republik Indonesia, “Intruksi Menteri Dalam Negeri Tentang Perpanjangan Pemberlakuan Pembatasan kegiatan Masyarakat berbasis Mikro dan mengoptimalkan Posko Pananganan Corona Virus Disease 2019 di Tingkat Desa dan Kelurahan Untuk Pengendalian Penyebaran Corona Virus Disease 20, pp. 1–19, 2021.

R. S. Putra, “Klasifikasi Penyebaran Covid-19 Menggunakan Algoritma C4.5 Kota Pagar Alam,” Jukomika, vol. 4, no. 1, pp. 23–35, 2021.

Z. I. Alfianti, “Pengelompokan Wilayah Penyebaran Covid-19 Di Kabupaten Karawang Menggunakan Algoritma K-Means,” J. Ilm. Inform. Komput., vol. 26, no. 2, pp. 111–122, 2021.

I. Cholissodin, F. M. Evanita, J. J. Tedjasulaksana, and K. W. Wahyuditomo, “Klasifikasi Tingkat Laju Data Covid-19 Untuk Mitigasi Penyebaran Menggunakan Metode Modified K-Nearest Neighbor (MKNN),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, p. 595, 2021.

S. Mujiasih, “Pemanfatan Data Mining Untuk Prakiraan Cuaca,” no. September, pp. 189–195, 2011.

M. Elkano, M. Galar, J. Sanz, and H. Bustince, “Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method,” Inf. Sci. (Ny)., vol. 332, pp. 94–114, 2016.

A. Borgi, “Attributes regrouping in Fuzzy Rule Based Classification Systems : an intra-classes approach,” in 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–7, 2018.

Menteri Dalam Negeri Republik Indonesia, “Intruksi Menteri Dalam Negeri No 58 Tahun 2021 Tentang Pemberlakuan Pembatasan Kegiatan Masyarakat Level 3, Level 2, Dan Level 1 Serta Mengoptimalkan Posko Penanganan Corona Virus Disease 2019 Di Tingkat Desa Dan Kelurahan Untuk Pengendalian Penyebaran Co,” Salinan, vol. 58. pp. 1–27, 2021.

S. Vluymans, Y. Saeys, L. D’Eer, and C. Cornelis, “Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey,” in Fundamenta Informaticae, vol. 142, no. 1–4, pp. 53–86, 2015

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


  • There are currently no refbacks.

J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats