Pattern Recognition of Customer Spending Habits Using Apriori Algorithms in DataMining as an Inventory Strategy

Authors

  • Arief Jananto  Information Systems Department, Faculty of Information Technology and Industry, Stikubank University, Semarang, Indonesia
  • Yohanes Suhari  Information Systems Department, Faculty of Information Technology and Industry, Stikubank University, Semarang, Indonesia
  • Rara Sriartati Redjeki  Information Systems Department, Faculty of Information Technology and Industry, Stikubank University, Semarang, Indonesia
  • Bambang Sudiyatno  Department of Management, Fakulty of Economics and Business, Stikubank University, Semarang, Indonesia

DOI:

https://doi.org/10.32628/CSEIT22868

Keywords:

Apriori, Inventory, Association Rules, Data Mining

Abstract

The readiness of product inventory is very important, product shortages related to other products can make buyers disappointed and then cancel to buy products that were previously planned to be purchased at once. Sellers can experience a decrease in the number of sales to revenue. In this case, the seller needs to know the pattern of customer habits when making purchases by going through sales transaction data that has occurred. Association techniques can be used to analyze the pattern of interrelationships between items in transaction events. With the a priori algorithm as a popular association algorithm, the pattern of sales transaction data can be analyzed through the research stage. From the implementation of the algorithm with 1063 transaction data using 10% min-support and 75% min-confidence resulting in 4 association rules where 1) if you buy "kacer" and "love bird" you will buy "pentet" as much as 17% support, 2) if you buy "magpie" and "love bird" will also buy "pentet" at 16%, 3) if you buy "kacer" and "magpie" then you will buy "pentet" at 14%, 4) if you buy "anis" you will buy "pentet" of 11% with a confidence level of 76%, 81%, 84%, 77%, respectively. So, there are 5 main items that play a strong role in the rule that must be considered. Sellers can use the resulting item relationship patterns as consideration in managing inventory and structuring the items sold.

References

  1. M. S. I. R. S. H. M. S. Adnyana, "Market Basket Analysis For Procurement Of Food Stock Using Apriori Algorithm And Economic Order Quantity," International Journal of Engineering and Emerging Technology, p. 149, 2020.
  2. R. Maman Novian, "Analysis of the Application of Customer Purchase Mining Data on Paint Sales Using Apriori Algorithm (Case Study:PT Indowarna Cemerlang Indonesia)," Bit-Tech, vol. 2, no. 3, p. 131, 2020.
  3. A. E. K. B. D. Laela Kurniawati, "Implementasi Algoritma Apriori untuk Menentukan Persediaan Spare Part Compressor," CESS(Jurnal Of Computer Engineering System and Acience) Vol. 4 No. 1 Januari 2019, pp. 6-10, 2019.
  4. L. S. Z. L. W. L. N. C. R. H. Li Zhou, "Study on a storage location strategy based on clustering and association algorithms," Soft Computing , vol. 24, no. 8, p. 5499–5516, 2020.
  5. P. M. H. Nurayni Sinabang, "Application of Data Mining for Sales Strategy at Ria Busana Using the A priori Algorithm," Login : Jurnal Teknologi Komputer, vol. 14, no. 2, pp. 121-127, 2020.
  6. D. E. S. A. L. A. S. Tutik Khotimah, "ASSOCIATION RULE MINING UNTUK MENEMUKAN POLA PEMILIK UMKM," Prosiding SNATIF, pp. 517-522, 2018.
  7. A. Anas, "Penggalian Kaidah asosiasi dalam Memilih Program Kegiatan Pendukung Mahasiswa STIE-GK Muara Bulian," MEDIASISFO Vol. 11, No.1, April 2017, pp. 709-722, 2017.
  8. C. M. A. A. F. D. J. Ahmad Heru Mujianto, "Consumer Customs Analysis Using the Association Rule and Apriori Algorithm for Determining Sales Strategies in Retail Central," in ICENIS, Semarang, 2019.
  9. A. F. T. Y. P. Sunardi, "Market Basket Analysis to Identify Stock Handling Patterns and Item Arrangement Patterns Using Apriori Algorithms," Jurnal Ilmu Komputer dan Informatika, vol. 6, no. 1, pp. 33-41, 2020.
  10. A. S. ,. A. B. M. I. F. Samuel, "SALES LEVEL ANALYSIS USING THE ASSOCIATION METHOD WITH THE APRIORI ALGORITHM," JURNAL RISET INFORMATIKA, vol. 4, no. 4, pp. 331-340, 2022.
  11. S. S. M. A. S. L. R. ,. H. T. S. B. S. Panjaitan, "Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns," in The International Conference on Computer Science and Applied Mathematic, Parapat, Indonesia, 2019.
  12. B. H. N. Vivek Ware, "Decision Support System for Inventory Management using Data Mining Techniques," International Journal of Engineering and Advanced Technology (IJEAT), vol. 3, no. 6, pp. 164-168, 2014.
  13. G. S. B. D. H. S. R. D. Alexander Setiawan, "Data Mining Applications for Sales Information System Using Market Basket Analysis on Stationery Company," in 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), Denpasar, Indonesia, 2018.
  14. E. S. A. A. A. B. A.L.SAYETH SAABITH, "PARALLEL IMPLEMENTATION OF APRIORI ALGORITHMS ON THE HADOOP-MAPREDUCE PLATFORM- AN EVALUATION OF LITERATURE," Journal of Theoretical and Applied Information Technology , vol. 85, no. 3, pp. 321-351, 2018.
  15. M. K. Jiawei Han, Data Mining: Concepts and Techniques, Second Edition, San Francisco: Morgan Kaufmann, 2006.
  16. B. Santoso, Data Mining : Teknik Pemanfaatan Data untuk Keperluan Bisnis, Yogyakarta: Graha Ilmu, 2009.
  17. E. T. L. Kusrini, Algoritma Data Mining, Yogyakarta: Andi Publiser, 2009.
  18. A. S. Hena Lisnawati, "Data Mining with Associated Methods to Predict Consumer Purchasing Patterns," I.J. Modern Education and Computer Science, vol. 5, pp. 16-28, 2020.
  19. A. D. S. H. S. S. J. W. Michael S Packianather, "Data mining techniques applied to a manufacturing SME," in 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16, 2017.
  20. D. M. P. Dr. M. Dhanabhakyam, "A Survey on Data Mining Algorithm for Market Basket Analysis," Global Journal of Computer Science and Technology, vol. 11, no. 11, 2011.
  21. M. Kang, "Market Basket Analysis: Identify the changingtrens of market data," Procedia Computer Science, pp. 78 - 85, 2016.
  22. E. Irfiani, "Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores," SINKRON : Journal Publications & Informatics Engineering Research, vol. 3, no. 2, pp. 218-222, 2019.
  23. D. A. Nurdin, "Penerapan Data Mining untuk Menganalisis Penjualan Barang dengan Menggunakan Metode Arpiori Pada Supermarket Sejahtera LhokSeumawe," Techsi Vol. 6 No.1, April 2015, pp. 133-155, 2015.
  24. H. F. A. S. Sanjani, "Implementasi Data Mining Penjualan Produk Pakaian Dengan Algoritma Apriori," IJAI(Indonesian Journal of Applied Informatics) Vol. 4 No. 1 Tahun 2019, pp. 23-29, 2019.
  25. R. N. Arifin, "Implementasi Algoritma Frequent Pattern Growth(FP-Growth) Menentukan Asosiasi antar produk(Studi Kasus : Nadiamart)," Jurnal Teknik ITS, pp. 68-76, 2015.
  26. C. D. L. Daniel T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Edition, Jhon Wiley & Sons Inc, 2005.
  27. E. Buulolo, Data Mining Untuk Perguruan Tinggi, Yogyakarta: Deepublish, 2020.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-12-30

Issue

Volume 8, Issue 6, November-December-2022

Section

Research Articles

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How to Cite

[1]
Arief Jananto, Yohanes Suhari, Rara Sriartati Redjeki, Bambang Sudiyatno, " Pattern Recognition of Customer Spending Habits Using Apriori Algorithms in DataMining as an Inventory Strategy" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.104-115, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT22868