FP GROWTH ALGORITHM MODELING FOR PRODUCT INVENTORY ANALYSIS
DOI:
https://doi.org/10.47701/icohetech.v4i1.3390Keywords:
FP Growth, Association Rule, Product InventoryAbstract
Pusaka Tani is a shop that provides agricultural needs such as fertilizers, rice seeds and plant medicines. Sales transactions at the Farmer's Library still use a manual system, namely by using notes as proof of sales transactions. The amount of data that accumulates results in less useful data. So that Pusaka Tani cannot know the products that are often purchased by consumers simultaneously which results in many products being sold out without the knowledge of Pusaka Tani which results when consumers are not going to buy these products and products that are rarely purchased by consumers become unsold. The purpose of this study is to model FP Growt in the analysis of product stock supply. In the research conducted, the support value and confidence value used is a minimum support value of 30% and a minimum confidence value of 70%. The results obtained from the research conducted are for the highest lift ratio value of 1.67 and the lowest is 1.09
References
Adani, N. F., Boy, A. F., Kom, S., Kom, M., Syahputra, R., Kom, S., & Kom, M. (2019). Data Mining Implementation for Grouping Sales Data Based on Purchasing Patterns Using the K-Means Clustering Algorithm at Syihan's Store. x, 1–11.
Asana, I. M. D. P., Wiguna, I. K. A. G., Atmaja, K. J., & Sanjaya, I. P. A. (2020). FP-Growth Implementation in Frequent Itemset Mining for Consumer Shopping Pattern Analysis Application. Journal of Mantik, 4(3), 2063–2070. http://iocscience.org/ejournal/index.php/mantik/article/view/882/595
Dewi Anisa Istiqomah, Yuli Astuti, & Siti Nurjanah. (2022). Implementation of FP-Growth and Apriori Algorithms for Product Inventory. Polinema Informatics Journal, 8(2), 37–42. https://doi.org/10.33795/jip.v8i2.845
Fenny Krisna Marpaung, Markus Willy Arnold S, Asyifa Sofira, S. A. (2021). The Effect of Price, Promotion, and Product Quality on Indomie Consumer Purchase Decisions at Pt. Alamjaya. 7, 49–64.
Gunarsih, C. M., Kalangi, J. A. F., & Tamengkel, L. F. (2021). The Effect of Price on Consumer Purchase Decisions at Pelita Jaya Buyungon Amurang Stores. Productivity, 2(1), 69–72. https://ejournal.unsrat.ac.id/index.php/productivity/article/view/32911/31075
Musyaffa, N., Prasetyo, A., & Sastra, R. (2021). Market Basket Data Mining Analysis of Sales Data Using the Frequent Pattern Growth (Fp Growth) Algorithm. Journal of Equator Informatics, 9(2), 115–120. https://doi.org/10.31294/jki.v9i2.11133
Nasyuha, A. H., Jama, J., Abdullah, R., Syahra, Y., Azhar, Z., Hutagalung, J., & Hasugian, B. S. (2021). Frequent pattern growth algorithm for maximizing display items. Telkomnika (Telecommunication Computing Electronics and Control), 19(2), 390–396. https://doi.org/10.12928/TELKOMNIKA.v19i2.16192
Rismayati, R., Ismail Marzuki, J., & Tapen, K. (2021). Implementation of the Frequent Pattern-Growth Algorithm for Graduate Student Patterns with Rapidminer. Journal of Informatics & Electronic Engineering), 4(2), 106–114. http://e-journal.stmiklombok.ac.id/index.php/jireISSN.2620-6900
Supriyatna, A., & Puspitasari, D. (2021). Implementation of Extreme Programming Method in Web Based Digital Report Value Information System Design. IJISTECH (International Journal of Information System & Technology), 5(1), 67. https://doi.org/10.30645/ijistech.v5i1.116
Wicaksono, D., Jambak, M. I., & Saputra, D. M. (2020). The Comparison of A priori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Patterns in Association Rule. 172(Siconian 2019), 315–319. https://doi.org/10.2991/aisr.k.200424.047
