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        <dc:title>(AIoCSIT - S2) - Priyanto, Rizqi, Hasan [2026-10-19]</dc:title>
        <dc:creator>Hasan, Firman Noor</dc:creator>
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        <dc:description>The current development of the automotive industry increases the need for after-sales services for motor vehicles, including the provision of spare parts. Boang Moto Shop, as an automotive MSME, faces challenges in stock management due to the suboptimal utilization of available sales transaction data. This study aims to analyze consumer purchasing patterns to find combinations of spare parts that are frequently purchased together. The method used is data mining by applying the Frequent Pattern Growth (FP-Growth) algorithm. Data processing is carried out through the RapidMiner application using sales transaction data for three months. The analysis process starts from the data preprocessing stage, FP-Tree formation, to the extraction of association rules based on support, confidence, and lift ratio parameters. The results of this study are expected to provide recommendations for Boang Moto Shop management in the form of product package preparation (bundling), store layout arrangement, and determining inventory procurement priorities more effectively and efficiently to minimize the risk of stockpiling or running out of spare parts</dc:description>
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        <dc:identifier>  Hasan, Firman Noor  (2026) (AIoCSIT - S2) - Priyanto, Rizqi, Hasan [2026-10-19].  AIoCSIT: Al'Adzkia International of Computer Science and Information Technology Journal, 7 (2).  pp. 111-120.  ISSN 2722-0001     </dc:identifier>
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