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PT Astra International BMW Semarang is a company operating in the automotive sector with 3 supporting pillars, namely Sales, Aftersales and Spare Parts for BMW car units. The availability of spare parts is one of the determining factors for consumer satisfaction with the company because if the spare parts stock is empty it will cause consumer disappointment with the company. By using spare parts sales transaction data for the period January 2019 – June 2023, totaling 52,162, it will be utilized using data mining association techniques with the a priori algorithm and the eclat algorithm. The problem in this research is how to find out consumer purchasing patterns so that there is no shortage or empty stock of spare parts in the warehouse. This research aims to determine the association of spare parts purchasing patterns in sales transactions so that partman get recommendations in making decisions about providing priority types of spare parts. This research methodology uses CRISP-DM (Cross-Industry Standard Process for Data Mining) and is implemented with the R programming language with R studio software. In 3 trials using the Apriori algorithm and 3 trials with the Eclat algorithm, The result with the highest confidence appears in a combination of 3 itemsets with minimum support 0.01 and confidence 0.9, namely if a customer buys B11.42.8.593.186 (Set oil-filter Mx) and B83.12.5.A1A.683 (Washer Cleaner) then they will also buy Z99000000333 ( BMW Engine Oil) with confidence 1.00 or 100%. From the results of this association's analysis, it can be used as advice for the management of PT Astra International BMW Semarang in managing spare parts stock.

PT Astra International BMW Semarang operates in the automotive sector, focusing on sales, aftersales, and spare parts for BMW cars. The availability of spare parts is crucial for customer satisfaction, as stock shortages can lead to disappointment. Using data from 52,162 spare parts sales transactions from January 2019 to June 2023, the study applies data mining techniques with the a priori and eclat algorithms to identify consumer purchasing patterns and prevent stock shortages. The research aims to provide recommendations for prioritizing spare parts stock. Utilizing the CRISP-DM methodology and R programming, the study found that the highest confidence in purchasing patterns occurs with a combination of three itemsets: if a customer buys an oil filter set (B11.42.8.593.186) and washer cleaner (B83.12.5.A1A.683), they will also buy BMW engine oil (Z99000000333) with 100% confidence. These findings can help PT Astra International BMW Semarang manage spare parts stock more effectively.