Entropi Tabanlı ELECTRE TRI ve K-Ortalamalar Yöntemleriyle Ülkelerin Refah Düzeyine Göre Değerlendirilmes
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2021
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info:eu-repo/semantics/openAccess
Abstract
Öz: Bu çalışmada amaç, ülkeleri Çok Kriterli Karar Verme (ÇKKV) ve Kümeleme Analizi (KA) yöntemleriyle refah düzeylerine göre gruplara ayırmak ve yöntemlerin uygulanabilirliğini göstermektir. Bu kapsamda; Legatum Refah Endeksi (LRE)’nde yer alan 12 refah göstergesi ve 167 ülkenin 2019 güncel verileri kullanılmıştır. Çalışmada, önce ÇKKV yöntemlerinden Entropi tabanlı ELECTRE TRI yöntemi uygulanarak ülkeler refah düzeylerine göre sınıflandırılmış ve dört grup elde edilmiştir. Bu dört grup küme sayısı olarak belirlenerek veriler KA yöntemlerinden olan K- Ortalamalar algoritmasıyla kümelere ayrılmıştır. Yöntemlerden elde edilen sonuçlar karşılaştırılarak grupların bu göstergeler dahilinde benzerlik gösterip göstermediği incelenmiş, Türkiye’nin bu gruplar içerisindeki yeri belirlenmiştir. Sonuçlar; yöntemlerin ülkeleri refah düzeyine göre ayırmada %73 oranında benzerlik gösterdiğini ortaya koymaktadır. Bununla birlikte göstergeler incelendiğinde her iki yöntemle de en çok etkisi olan göstergelerin “pazar erişimi ve alt yapı, eğitim” olduğu; buna karşılık en az etkisi olan göstergelerin ise “sosyal sermaye, doğal çevre” olduğu belirlenmiştir
Öz: The aim of this study is to divide countries into groups according to their prosperity levels with Multi Criteria Decision Making (MCDM) and Cluster Analysis (CA) methods and to show the applicability of the methods. In this context; The 2019 updated data of 12 prosperity indicators and 167 countries in The Legatum Prosperity Index (LPI) were used. In the study, firstly, Entropy-based ELECTRE TRI method, one of the MCDM methods, was applied, and countries were classified according to their prosperity levels and four groups were obtained. These four groups were determined as the number of clusters and the data were divided into clusters with the K-Means algorithm, which is one of the CA methods. Comparing the results obtained from the method of the groups was examined whether they show similarities within these indicators, Turkey was determined place in these groups. Results; It reveals that the methods are 73% similar in separating countries according to their prosperity level. However, when the indicators are examined, it is seen that the indicators that have the most impact with both methods are "market access and infrastructure, education"; On the other hand, it has been determined that the indicators with the least impact are "social capital, natural environment
Öz: The aim of this study is to divide countries into groups according to their prosperity levels with Multi Criteria Decision Making (MCDM) and Cluster Analysis (CA) methods and to show the applicability of the methods. In this context; The 2019 updated data of 12 prosperity indicators and 167 countries in The Legatum Prosperity Index (LPI) were used. In the study, firstly, Entropy-based ELECTRE TRI method, one of the MCDM methods, was applied, and countries were classified according to their prosperity levels and four groups were obtained. These four groups were determined as the number of clusters and the data were divided into clusters with the K-Means algorithm, which is one of the CA methods. Comparing the results obtained from the method of the groups was examined whether they show similarities within these indicators, Turkey was determined place in these groups. Results; It reveals that the methods are 73% similar in separating countries according to their prosperity level. However, when the indicators are examined, it is seen that the indicators that have the most impact with both methods are "market access and infrastructure, education"; On the other hand, it has been determined that the indicators with the least impact are "social capital, natural environment
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Akdeniz İİBF Dergisi
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21
Issue
2
Citation
ŞENER S,YILMAZ Ş. K (2021). Entropi Tabanlı ELECTRE TRI ve K-Ortalamalar Yöntemleriyle Ülkelerin Refah
Düzeyine Göre Değerlendirilmes. Akdeniz İİBF Dergisi, 21(2), 191 - 209. Doi: http://dx.doi.org/10.25294/auiibfd.888980
