Principal Component Analysis and Agglomerative Hierarchical Clustering for Assessing the Condition of MSMEs Assisted by the Department of Cooperatives and MSMEs - Dewi, Ardiana Fatma; Ahadiyah, Kurnia (Jurnal Varian, 2026)
Usaha Mikro, Kecil dan Menengah (UMKM) have an important role in the growth of the Indonesian economy. To achieve these hopes certainly requires a strategy. One way is to formulate policies based on information adapted to local conditions. One of the right ways to conduct this research is through data mining. There are techniques in data mining and one of the techniques that can be used is clustering with the Agglomerative Hierarchical Clustering Algorithm with Principal Component Analysis (PCA). Cluster analysis aims to group objects based on their characteristics. This research aims to determine the appropriate distribution strategy for business capital assistance. In grouping UMKM assisted by the Department of Cooperatives and UMKM of Kediri City based on several indicators measured by business capital, turnover, profits, human resources, marketing methods, government capital assistance, type of business, and place of business, it was found that the optimal algorithm used was complete linkage. With a cophenetic correlation value obtained of 0.733. Based on good internal cluster validation through silhouette values ​​based on the characteristics possessed by UMKM actors, the number of representative clusters is 3 clusters. An interesting finding is that the third cluster has not had access to government assistance programs. Based on the results of this research, it can be concluded that the allocation of government capital assistance is not fully evenly distributed and is not optimal in achieving the goal of increasing the competitiveness of UMKM.