Penerapan Algoritma K-Means dan K-Medoids untuk Mengelompokkan Data Negara Berdasarkan Faktor Sosial-Ekonomi dan Kesehatan
Application of K-Means and K-Medoids Algorithms for Grouping Country Data Based on Socio-Economic and Health Factors
Keywords:
country, healths, k-means, k-medoids, socio-economicAbstract
Increased development for a country can be assessed from the welfare of its people, therefore people's welfare is one of the goals for a country. The increasing welfare of the people in a country, shows the increasing development of development in that country. Assessment of development progress in a country can be seen from various factors, for example socio-economic factors and health. Non-Governmental Organizations (NGOs) have managed to raise around $10 million or the equivalent of 156 billion to be given to countries in need. However, they find it difficult to determine which countries need the assistance the most. As a result, this study employs data mining to categorize country datasets based on economic-social and health aspects by applying the K-Means algorithm compared to the K-Medoids algorithm. The purpose of the grouping is to show countries that need assistance, so that existing costs can be used strategically and effectively. According to the study, K-Means performs better than K-Medoids when clustering nation data using the K-Means and K-Medoids algorithms. The cluster results are determined using the Davies Bouldin Index (DBI), and K-Means has a DBI value of 0.095 and K = 5, where the validity value is near to 0.
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