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Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .

Improving the quality of Human Resources (HR) is a major challenge in facing global competition. Education as the main means of improving the quality of HR in Indonesia is still faced with the problem of inequality of access and quality between regions. This inequality causes disparities in educational development between urban and remote areas. This study focuses on grouping provinces in Indonesia based on aspects of educational development in 2023, using cluster analysis. Secondary data from the Central Statistics Agency (BPS) is used as the basis for analysis, including variables of average length of schooling, Gross Participation Rate (APK), Pure Participation Rate (APM), number of senior high schools, and community literacy development index. This study uses hierarchical and non-hierarchical cluster analysis methods to group provinces in Indonesia. The results of the hierarchical cluster analysis using the average linkage method show the most optimal cluster with the formation of three clusters. The first cluster consists of 31 provinces, the second cluster consists of 2 provinces, and the third cluster consists of 1 province. Data characteristics show large variations in the number of senior high schools and relative homogeneity in the average length of schooling between provinces.

Community welfare is a primary objective of national development, encompassing various aspects such as health, education, and decent employment, all of which play crucial roles in achieving national stability and progress. However, welfare is not solely dependent on economic factors but also on the overall quality of life. Unfortunately, disparities in welfare persist across different regions, influenced by local environmental factors, including access to education, which in turn affects job opportunities and income levels. Inequalities in employment opportunities can potentially slow down national development by reducing the number of individuals capable of contributing productively to key economic sectors. To enhance national development, further analysis of welfare indicators such as the open unemployment rate, human development index, labor force participation rate, and poverty levels is essential. Therefore, this study conducts cluster analysis on welfare indicators across districts/cities in Central Java for the year 2023. Both hierarchical and non-hierarchical (K-Means) clustering methods are employed to identify patterns of inequality by partitioning data into groups based on specific similarities. This approach facilitates a more effective review of policies to address welfare disparities across various regions. The findings indicate that the welfare indicators in Central Java are in a relatively poor condition, with low labor force participation rates, low human development indices, and high poverty rates. The hierarchical and non-hierarchical cluster analysis identified 5 optimal clusters, with all welfare variables having significant influence, requiring four iterations to reach the final centroids.