IMPLEMENTATION OF THE ASSOCIATION RULE METHOD AND FP-GROWTH ALGORITHM FOR INVENTORY CONTROL
Keywords:
Association Rule Mining, FP-Growth, pengendalian persediaan, inventaris TI, frequent itemset, aturan asosiasiAbstract
Inventory control is an essential component of supply chain management, as it ensures the optimal availability of goods to meet organizational needs while preventing both overstocking and stock shortages. In the field of Information Technology (IT), inventory management is more complex due to the need to handle various hardware, software, and supporting components with large volumes of data and rapidly evolving technologies. Traditional inventory control methods have limitations in processing large-scale data and identifying relationships among inventory items, often resulting in inaccurate stock predictions. Therefore, this study proposes the implementation of the Association Rule Mining (ARM) method using the FP-Growth algorithm to optimize inventory control in an IT environment. The research focuses on investigating how the FP-Growth algorithm can identify relationships among inventory items and improve the effectiveness of inventory management compared to traditional methods. The objective of this study is to identify frequent itemsets and association rules based on inventory transaction data in order to provide more effective and efficient inventory control recommendations. The research methodology includes inventory data collection, data preprocessing through cleaning and transformation, FP-tree construction, frequent itemset mining based on a minimum support threshold, and association rule generation using support, confidence, and lift ratio measures. The resulting association rules are then analyzed to support decision-making in inventory control. This research is developed based on previous studies demonstrating that the FP-Growth algorithm improves inventory management accuracy, reduces storage costs, and is more efficient than the Apriori algorithm in processing large datasets. The implementation of this method is expected to produce a data-driven inventory control system that is more effective, efficient, and adaptive to the needs of organizations in the Information Technology environment.
Keywords: Inventory Control, Association Rule Mining, FP-Growth Algorithm, Data Mining, Information Technology Inventory.
References
Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” SIGMOD Record, vol. 29, no. 2, pp. 1–12, 2000, doi: 10.1145/335191.335372.
R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in Proc. 20th Int. Conf. Very Large Data Bases (VLDB), 1994, pp. 487–499.
C. Borgelt, “An implementation of the FP-growth algorithm,” in Proc. 1st Int. Workshop Open Source Data Mining: Frequent Pattern Mining Implementations, 2005.
I. Riadi, F. Herman, M. Fitriah, A. Muis, et al., “Implementation of association rule using apriori algorithm and frequent pattern growth for inventory control,” vol. 15, no. 4, pp. 369–378, 2023, doi: 10.20895/infotel.v15i4.980.
I. Riadi, F. Herman, M. Fitriah, and Suprihatin, “Optimizing inventory with frequent pattern growth algorithm for small and medium enterprises,” MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 1, pp. 169–182, 2023, doi: 10.30812/matrik.v23i1.3363.
D. Rachmawati et al., “Perbandingan algoritma Apriori dan algoritma FP-Growth dalam menentukan pola penjualan pupuk,” vol. 3, no. 1, pp. 21–31, 2024.
I. F. Rahman and D. Riana, “Market basket analysis untuk penjualan retail: Perbandingan akurasi,” pp. 468–479, 2025, doi: 10.33364/algoritma/v.22-1.2303.
S. Komariyah, S. Anwar, and B. Nurhakim, “Implementasi data mining FP-Growth untuk analisis pola pembelian pada transaksi penjualan,” vol. 1, no. 2, 2023.
H. Chen, M. Yang, and X. Tang, “Association rule mining of aircraft event causes based on the Apriori algorithm,” Scientific Reports, pp. 1–18, 2024, doi: 10.1038/s41598-024-64360-6.
H. Khalid and V. Wade, “Comparative analysis of existing and a novel approach to topic detection on conversational dialogue data,” vol. 11, no. 4, pp. 1–18, 2022.
H. Sahara, S. D. Saragih, and A. P. Windarto, “Teknik asosiasi datamining dalam menentukan pola penjualan dengan metode Apriori,” vol. 2, no. 12, pp. 684–689, 2022, doi: 10.47065/tin.v2i12.1577.
N. D. Sari and S. Khoiriah, “Penerapan metode asosiasi pada Toko Afifa dengan algoritma Apriori,” vol. 1, no. 1, pp. 8–17, 2022.
H. Anderson et al., “Penerapan data mining asosiasi genre game untuk market research,” 2022.
H. Ilmiah and J. Pendidikan, “Penerapan data mining pada pengelolaan data,” vol. 10, no. 16, pp. 655–661, 2024.
F. Rahmawati and N. Merlina, “Metode data mining terhadap data penjualan sparepart mesin fotocopy menggunakan algoritma Apriori,” PIKSEL: Penelitian Ilmu Komputer Sistem Embedded and Logic, vol. 6, no. 1, pp. 9–20, 2018, doi: 10.33558/piksel.v6i1.1390.
