Analisa Data Mining Menggunakan Algoritma Apriori Dan Algoritma Eclat Di PT Astra International BMW Semarang

Abstract

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.