<![CDATA[So far, small and medium enterprises (MSMEs) have shown a contribution of 61.7% to GDP, or IDR 8,573.89 trillion. Apart from that, MSMEs can absorb around 97% of the national workforce. However, in reality, MSMEs face many problems, one of the most common being a lack of business capital. One of the factors causing the slow growth of MSMEs in Indonesia is development policies that are not on target. This is especially true for this research in West Aceh District. Each MSME is unique, making it difficult for banking institutions to establish consistent financing policies. This research aims to map the characteristics of MSMEs in the form of groups to make it easier to determine policy-making patterns. The Hierarchical Data Mining Clustering Method is considered appropriate because it has a much lower bias than K-means. Apart from that, this method can reduce data complexity. According to the results of data distribution for MSMEs in the fisheries sector using Matlab 2016b software, there are 6 clusters, and the results of data distribution for MSMEs in the trade sector using Matlab 2016b software show 7 clusters. Each cluster has main parameters that make MSMEs superior, such as length of business, capital ownership, sales projections, and average sales. For the last parameter.]]>
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