<![CDATA[Employee turnover can disrupt the organization's operations and more or less cause losses to the business. Therefore, it is important to understand the causal factors so that organizations can take anticipatory action. Identify reasons employees leave their jobs is crucial for both employers and policy makers, especially when the goal is to prevent this from happening. Data on the causes of employee turnover is complex data that can have many dimensions, so a certain method is needed to analyze it. In this research, an analysis of data on the causes of employee turnover with 10 dimensions will be carried out using the Self Organizing Map (SOM) method. The Self-Organizing Map (SOM) is a technique for clustering and visualizing high-dimensional data by mapping it to a two-dimensional space while preserving the data's topological structure. This neural network-based method ensures that similar data points remain close to each other in the resulting 2D representation. SOM will cluster the data into several uniform groups. The results of this SOM grouping will be assessed with the Silhouette score, Dunn index and Connectivity value to determine how uniform the grouping is. Hopefully that by using the results of this SOM grouping, it shows that the clusters formed are very good and the data is clearly grouped. Therefore, we can analyze these groups with more accurate results.]]>
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