<![CDATA[The effective management of employee data plays an important role in supporting organizational decision-making, particularly in today’s data-driven business environment. This study examines the identification of association patterns within employee data at Kalla Toyota by applying a combined approach using the Apriori, ECLAT, and FP-Growth algorithms. The dataset includes information such as demographic characteristics, educational background, marital status, and job classification. Prior to analysis, the data were carefully preprocessed to improve consistency and ensure suitability for pattern discovery. Relevant variables were then selected using statistical measures, including Cramér’s V, Kendall’s Tau, and Chi-Square tests, to capture meaningful relationships among attributes. With a minimum support threshold set at 10%, the combined method produced 84 association rules considered significant. These patterns were further explored using visual tools such as network graphs and matrix plots to better understand the relationships between variables. The findings highlight notable connections among factors such as gender, generational groups, job roles, and marital status. These insights may assist the company in refining its human resource strategies, particularly in areas such as recruitment, employee development, and retention. This study shows that combining multiple association rule techniques can provide a more comprehensive understanding of employee data and support more informed decision-making]]>
