Klasifikasi negara melalui model pandemi COVID-19 dengan parameter dinamis = Country classification through the covid-19 pandemic model with dynamic parameters

Himawan, Jessica Cheryl (2021) Klasifikasi negara melalui model pandemi COVID-19 dengan parameter dinamis = Country classification through the covid-19 pandemic model with dynamic parameters. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

COVID-19 merupakan penyakit menular yang menyebar cepat ke banyak negara di dunia. Penyakit ini menyerang sistem pernapasan sehingga dapat membahayakan. Penderita COVID-19 dapat memiliki gejala yang serius ataupun tanpa gejala. Penyebarannya dapat berkurang dengan melakukan social distancing. Dalam skripsi ini, dibuat model untuk COVID-19 dengan menggunakan model SIR (Susceptible, Infectious, Recovered). Perhitungan parameter COVID-19 didapat dengan menggunakan data kasus COVID-19 di setiap negara dari kasus pertama di negara tersebut hingga bulan September. Dipilih 29 negara yang dengan presentase tes COVID-19 terbanyak pada negara yang paling terkena dampak COVID-19 untuk diestimasi parameternya. Perhitungan parameter β untuk COVID-19 dilakukan dalam dua variasi. Variasi pertama dengan mengestimasi parameter β dari model SIR dan menetapkan nilai parameter γ. Variasi kedua dengan mengestimasi parameter β dan γ dari model SIR. Parameter β dan γ merupakan parameter dinamis yang berubah setiap 14 hari. Setiap parameter β diklasifikasikan menggunakan k-means clustering. Selain itu ditambahkan lima parameter lain yaitu Compliance Risk Index (CRI), Economic Complexity Index (ECI), Anti-Money Laundering (AML), Gross Domestic Product (GDP) per capita, dan School Enrollment. Kelima parameter ini beserta parameter COVID-19 juga diklasifikasi menggunakan k-means clustering. Dari hasil clustering akan dilihat kelompok parameter yang paling sesuai dengan parameter β sebagai parameter perkembangan COVID-19. / COVID-19 is a contagious disease that is spreading rapidly to many countries in the world. This disease attacks the respiratory system so that it could be dangerous. People with COVID-19 can have serious or no symptoms. Its spread can be reduced by doing social distancing. In this thesis, a model for COVID-19 is made using the SIR (Susceptible, Infectious, Recovered) model. The calculation of COVID-19 parameters is obtained by using data on COVID-19 cases in each country from the first case in each country until month of September. The 29 countries which is affected with the highest percentage of COVID-19 tests were selected to estimate the parameters. The calculation of the β parameter for COVID-19 is carried out in two variations. The first variation is to estimate the β parameter from the SIR model and set the parameter value γ. The second variation is to estimate the parameters β and γ from the SIR model. Parameters β and γ are dynamic parameters that change every 14 days. Each β parameter is classified using k-means clustering. In addition, five other parameters are added, namely the Compliance Risk Index (CRI), the Economic Complexity Index (ECI), Anti-Money Laundering (AML), Gross Domestic Product (GDP) per capita, and School Enrollment. These five parameters along with the COVID-19 parameters are also classified using k-means clustering. From the clustering results, it will be seen that the parameter group that most closely matches parameter β as a parameter for the developing of COVID-19.

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