Penerapan Data Mining dengan Metode Association Rule Menggunakan Algoritma Apriori Untuk Analisis Pola Barang Minus Bernilai Tinggi
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1299Keywords:
Data Mining; Association Rule; Apriori Algorithm; Missing Items; Inventory ControlAbstract
Indomaret stores, as part of the modern retail sector, face a high risk of inventory discrepancies, particularly high-value stock losses that may cause significant financial impacts. In practice, audit data are often analyzed manually, making it difficult to identify association patterns among missing items. This study aims to implement data mining using the association rule method with the Apriori algorithm to analyze patterns of high-value missing items at Indomaret stores (case study: IC Team Ajat Sugiantoro). The dataset was obtained from inventory audit results during April to June 2025, focusing on NK-category high-loss items. The analysis was conducted by generating frequent itemsets and association rules based on a minimum support threshold of 30% and a minimum confidence threshold of 70%. The results indicate that the Apriori algorithm can effectively identify relationships among missing items that frequently occur together, which can be used to prioritize stock monitoring and support preventive actions against inventory discrepancies. In addition, this research developed a web-based system that enables users to upload audit data, set support and confidence parameters, and automatically display the association rule results in a more systematic and efficient manner.
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