Implementasi Algoritma FP-Growth Pada Data Penjualan Produk Cetakan
Article Sidebar
Main Article Content
Abstract
The increasing volume of sales transaction data in the printing industry has driven the need for data mining techniques to identify customer purchasing patterns accurately and efficiently. This study aims to implement the FP-Growth algorithm on printed product sales data to discover frequent itemsets and association rules that can be used as a basis for business decision making. The research method uses the Knowledge Discovery in Databases (KDD) approach which includes the stages of transaction data collection, pre-processing through nominal to binominal transformation, application of the FP-Growth algorithm, formation of association rules, and evaluation using support, confidence, and lift metrics. The analysis process is carried out with the help of RapidMiner software. The results of the study indicate that the FP-Growth algorithm successfully identified strong association patterns between product type, price, order quantity, and total transactions. Several association rules generated have high confidence values, including the rule Price = 1700 → Order Quantity = 1000 with a confidence value of 0.753, the rule Product Type = GreaseProof → Order Quantity = 1000 with a confidence value of 0.731, as well as a number of deterministic rules that have a confidence value of 1,000, such as Order Quantity = 1000 and Price = 1700 → Total = 1,700,000. These findings indicate the consistency of customer purchasing patterns in printed product sales transactions. The results of the study prove that the FP-Growth algorithm is effective in revealing meaningful sales patterns and can be utilized to support promotional strategies, production planning, and more optimal inventory management in the printing industry.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499. https://dl.acm.org/doi/10.5555/645920.672836
Borgelt, C. (2012). Frequent item set mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 437–456. https://doi.org/10.1002/widm.1074
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54. https://doi.org/10.1609/aimag.v17i3.1230
Fu, Y., Han, J., Chiang, R., & Lin, J. (2007). Efficient incremental mining of frequent patterns using FP-tree. IEEE Transactions on Knowledge and Data Engineering, 19(3), 451–464. https://doi.org/10.1109/TKDE.2007.39
Gupta, M., Chandra, S., & Jain, A. (2021). Optimized FP-Growth algorithm for large transactional databases. Journal of Big Data, 8(1), 1–18. https://doi.org/10.1186/s40537-021-00445-2
Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 1–12. https://doi.org/10.1145/342009.335372
Park, S., Kim, J., & Lee, H. (2021). Parallel FP-Growth algorithm for big data analysis using MapReduce. Journal of Supercomputing, 77, 11564–11583. https://doi.org/10.1007/s11227-021-03806-8
Tan, P.-N., Steinbach, M., & Kumar, V. (2018). Introduction to Data Mining (2nd ed.). Pearson Education.
Zhang, Y., Wu, X., & Zhu, X. (2012). Mining frequent patterns with batch incremental updates. Knowledge-Based Systems, 35, 1–12. https://doi.org/10.1016/j.knosys.2012.04.002
Aqliyah, R., et al. (2025). FP-Growth algorithm implementation for association model optimization. International Journal of Advanced Computer Science and Applications, 16(2), 215–223.
Rustam, A., Handoko, P., & Agusstewan, F. (2024). Penerapan algoritma FP-Growth pada data penjualan untuk analisis pola pembelian. Jurnal Komputer dan Teknologi Informasi, 12(1), 45–54. https://jkomtekinfo.org