@misc{eprints86269,
           month = {April},
           title = {
CLUSTER ANALYSIS OF WARD AND K-MEANS METHODS TO GROUP
REGENCY-CITY IN WEST JAVA PROVINCE BASED ON
PARTICIPATION OF WOMEN IN DEVELOPMENT  },
          author = {NURKHOLIZ RAHMA },
         address = {UNIVERSITAS LAMPUNG},
       publisher = {FAKULTAS KEGURUAN ILMU PENDIDIKAN},
            year = {2025},
             url = {http://digilib.unila.ac.id/86269/},
        abstract = {
Cluster analysis is one of the multivariate statistical methods used to group objects
based on their similar characteristics without prior labeling. In this study, cluster
analysis is used to group regencies-cities in West Java Province based on women's
participation in development in 2021-2023 using the Ward and K-Means cluster
methods. To overcome the problem of multicollinearity between variables, data
transformation is carried out using Principal Component Analysis (PCA) in order
that the resulting new variables are independent to each other. The principal
component scores were used for clustering the regencies-cities data using the two
cluster methods, and the results were then evaluated using the Davies-Bouldin
Index (DBI) and Calinski-Harabasz Index (CHI) to determine the optimal number
of clusters. The results show that both the Ward and K-Means methods produce the
same number of optimal clusters, which are four clusters for data 2021, three
clusters for data 2022 and 2023, and four clusters for the average of data 2021-
2023. 

Keyword: Cluster Analysis, Ward, K-Means, PCA, Davies-Bouldin Index,
Calinski-Harabasz Index, Women's Participation.
 }
}