K-MEANS CLUSTERING

Dalam era informasi saat ini, memahami faktor-faktor yang mempengaruhi kebahagiaan global menjadi semakin penting bagi para pembuat kebijakan, peneliti, dan masyarakat umum. World Happiness Report (WHR) merupakan salah satu sumber data utama yang memberikan wawasan tentang tingkat kebahagiaan di berbagai negara berdasarkan berbagai indikator seperti GDP per capita, dukungan sosial, harapan hidup sehat, dan kebebasan untuk membuat pilihan hidup.

Untuk menganalisis dan mengelompokkan data kebahagiaan secara efektif, metode clustering menjadi alat yang sangat berguna. Salah satu teknik clustering yang populer adalah k-means clustering.

library(tidyverse)  # data manipulation
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library(cluster)    # clustering algorithms
library(factoextra) # clustering algorithms & visualization
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library(tibble)
library(MVN)
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DATASET

data <- read.csv(file = "C:/Users/acer/Downloads/archive (11)/2019.csv",header=T,sep=",")
head(data)
##   Overall.rank Country.or.region Score GDP.per.capita Social.support
## 1            1           Finland 7.769          1.340          1.587
## 2            2           Denmark 7.600          1.383          1.573
## 3            3            Norway 7.554          1.488          1.582
## 4            4           Iceland 7.494          1.380          1.624
## 5            5       Netherlands 7.488          1.396          1.522
## 6            6       Switzerland 7.480          1.452          1.526
##   Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1                   0.986                        0.596      0.153
## 2                   0.996                        0.592      0.252
## 3                   1.028                        0.603      0.271
## 4                   1.026                        0.591      0.354
## 5                   0.999                        0.557      0.322
## 6                   1.052                        0.572      0.263
##   Perceptions.of.corruption
## 1                     0.393
## 2                     0.410
## 3                     0.341
## 4                     0.118
## 5                     0.298
## 6                     0.343

Data merupakan data sekunder yang didapatkan dari website Kaggle yang berisikan variabel-variabel yang berpengaruh terhadap nilai kebahagian global tahun 2019.

# mengambil variabel numerik
index <- data[, 4:9] 
#rownames(index) <- data[1:156]

Preprocesing Data

# cek apakah ada atau tidaknya NA
index %>% 
  anyNA()
## [1] FALSE
# Cek seberapa banyak jumlah NA pada masing-masing kolom
index %>% 
  is.na() %>% 
  colSums()
##               GDP.per.capita               Social.support 
##                            0                            0 
##      Healthy.life.expectancy Freedom.to.make.life.choices 
##                            0                            0 
##                   Generosity    Perceptions.of.corruption 
##                            0                            0

Mengecek Multikolinearitas

cor=cor(index)
vif=diag(solve(cor))
vif
##               GDP.per.capita               Social.support 
##                     4.115838                     2.735651 
##      Healthy.life.expectancy Freedom.to.make.life.choices 
##                     3.572728                     1.575090 
##                   Generosity    Perceptions.of.corruption 
##                     1.224101                     1.431594

Penentuan Jumlah Clustering

fviz_nbclust(index, kmeans, method="silhouette")

#### Clustering dan Visualisasi

# k-means dengan 2 cluster
RNGkind(sample.kind = "Rounding")
## Warning in RNGkind(sample.kind = "Rounding"): non-uniform 'Rounding' sampler
## used
set.seed(100)

k3 <- kmeans(x= index , centers = 2, nstart = 50)

#visualiasi
fviz_cluster(k3, data = index)

165 Negara berhasil di clustering dalam 2 kelompok. Dimana kelompok tersebut masing-masing memiliki karakteristik dan dan kemiripannya masing-masing berdasarkan faktor yang ada.