Multikolinearitas adalah hubungan linear yang ada di antara variabel independen, pada analisis klaster efek yang ditimbulkan oleh multikolinearitas berbeda, dikarenakan pada dasarnya multikolinearitas adalah bentuk pembobotan implisit. Analisis komponen utama dapat digunakan untuk mereduksi jumlah himpunan peubah yang banyak dan saling berkorelasi menjadi peubah-peubah baru yang tidak berkorelasi dengan mempertahankan sebanyak mungkin keragaman data tersebut, dengan menggunakan hasil analisis komponen utama dilakukan analisis klaster menggunakan metode average linkage dan Ward, yang kemudian akan dipilih metode terbaiknya berdasarkan nilai indeks Dunn dan indeks RS, didapat kesimpulan bahwa metode Ward adalah metode terbaik dibandingkan average linkage yang ditinjau berdasarkan indeks RS, sedangkan dengan menggunakan indeks Dunn didapatkan kesimpulan bahwa metode average linkage adalah metode terbaik dibandingkan Ward.

COVID-19 pandemic is described as the most challenging crisis that humans have faced since World War II. From December 2019 until August 2021 based on the dataset provided by WHO, globally 219 countries in the world are affected by this virus. There are 205.338.159 cases cumulative total and 4.333.094 death cumulative total caused by this virus. In this paper, the data of 219 countries are analyzed using a robust clustering method namely K-Medoids cluster analysis.  Based on the result, 219 countries in the world can be divided into five clusters based on four COVID-19-related variables, i.e. the number of cases cumulative total, death cumulative total, positive cases per capita, and case fatality rate. The distribution of the countries in five clusters was as follows; the first cluster contained 48 countries, the second cluster contained 3 countries, the third and fourth clusters contained 16 and 89 countries respectively, and the last cluster contained 63 countries. The largest cluster is the fourth one, containing countries that form a cluster with a centroid below the world average, and the smallest cluster is the second cluster with the high cases in all attributes, consisting of the USA, India, and Brazil.

Indonesia is one of the most natural disaster-prone countries in the world, frequently exposed to a range of hazards. Currently, Indonesia has 34 provinces and natural disasters that occur in each province are different, therefore it is necessary to analyze the mapping of natural disasters that often occur in each province to provide scientific analysis for risk management of the natural disasters. One of the quick steps in describing data that can be used is biplot analysis, as biplot analysis can describe a lot of data then summarized it into the form of a two-dimensional graph. The aim of this research is to map 34 provinces in Indonesia based on the incidence of natural disasters from 2019 to 2021 using robust biplot analysis. Based on the result, robust biplot analysis can explain 87,9% of the information on natural disasters in every province in Indonesia. Lampung, Bengkulu, Bangka Belitung, Special Region of Yogyakarta, North Sulawesi, West Sulawesi, Southeast Sulawesi, Gorontalo, East Nusa Tenggara, Bali, Maluku, West Maluku, Papua, and West Papua are provinces that have similar natural disaster characteristics. Flood, tornado and forest and land fires are natural disasters that often occur in Indonesia. The provinces that have the highest risk of flood, landslide, and tornado were West Java, Central Java, and East Java. Then, the provinces with the highest risk of forest and land fires were Aceh and South Kalimantan.