CD Skripsi
Penerapanmetode Analisis Komponen Utama Dan Biplot Pada Indikator Kesejahteraan Rakyat Di Indonesia
welfare of the people is something that every country wants to achieve,
including Indonesia. This is certainly a special concern for the government to
achieve people's welfare in each province. Many factors that affect people's
welfare based on people's welfare indicators need an analysis that can see the
relationship between two or more variables, namely multivariate analysis, one of
which is the Principal Component Analysis (PCA) method. This study aims to
reduce the variables so that a principal component is formed which still represents
the original variable information from the people's welfare indicator data using the
PCA method and make a biplot based on the PCA results. This study uses 10
variables based on indicators of community welfare, namely population,
employment, housing and the environment, education, and information and
communication technology. By using the covariance variance matrix in the PCA
analysis stage, three principal component are obtained with a total cumulative
variance of 85% which can explain the overall information of the original
variables. Furthermore, biplot analysis was carried out to visualize the two PC, so
that four groups were formed to describe the relationship between provinces and
variables with a biplot goodness test result of 77%.
Keywords: Multivariate analysis, principal component analysis, variance
covariance matrix, biplot, indicators of people's welfa
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