Pengelompokan Negara Berdasarkan Indikator Kesejahteraan Dengan Metode Unsupervised Learning-Clustering: Bukti Empiris dari 167 Negara

Imaduddin Farih(1*), Lukman Fadillah(2), N Nadira(3), Verry Dina Aromy(4), Harry Patria(5),


(1) Institut Teknologi Sepuluh Nopember
(2) Institut Teknologi Sepuluh Nopember
(3) Institut Teknologi Sepuluh Nopember
(4) Institut Teknologi Sepuluh Nopember
(5) Institut Teknologi Sepuluh Nopember
(*) Corresponding Author

Abstract


One of the goals of the countries is to do continuous development in a positive direction so that the welfare of the country is guaranteed. To assess the development of a country can be seen from various factors such as socioeconomic and health factors. Some of the indicators used including GDP, health, income, export-import and others. This analysis can be used an evaluation of each country to improve its level. In addition, it is also used as a basis for determining which countries are entitled to receive assistance from funding institutions, so that the people of these countries can have a better life. Based on these problems, the authors analyze data of countries in the world using the Machine Learning Unsupervised which is Clustering method with KNIME. This analysis aims to determine the effect of indicators on the level of a country. The data to be studied are 167 countries in the world with socioeconomic and health factors. Based on research to avoid multicolenarity the authors use the PCA method. From this study, the authors used 4 PCA which represented 90% of the data and obtained 3 optimal clusters with an average silhouette value of 0.443.

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DOI: http://dx.doi.org/10.30645/j-sakti.v6i1.423

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