The Optimizing Sales Strategies to Address Excessive Stock Accumulation: A Data Mining Approach

Susandri; Muhammad Arief Solihin; Hamdani; Asparizal

doi:10.33372/jaia.v4i1.1110

Susandri STMIK Amik Riau http://orcid.org/0000-0002-3875-9747
Muhammad Arief Solihin STMIK Amik Riau
Hamdani STMIK Amik Riau
Asparizal Universitas Dumai

DOI: https://doi.org/10.33372/jaia.v4i1.1110

Keywords: Data mining, Aassociation-rule-apriori, Tenun

Abstract

The Two Pelita Weaving Business has recorded significant sales in the weaving industry, despite facing challenges in managing product stock due to the accumulation of excess stock caused by a lack of customer interest. This study employs data mining techniques, specifically the Association Rule and Apriori algorithms, to analyze sales patterns. The analysis results using Python and Orange Data Mining showed consistency in the relationship between Siku Keluang Weaving and Pucuk Rebung Weaving products, with high occurrence rates of purchase patterns (11.74% and 10%, respectively). High confidence levels with Python at 96.36% and Orange Data Mining at 99.1% indicate that customers who purchase Siku Keluang Weaving are also likely to purchase Pucuk Rebung Weaving products.

Author Biography

Susandri, STMIK Amik Riau

56979390700 b-9G49kAAAAJ 206414

References

[1] B. E. Pratiwi, “Motif Pucuk Rebung pada Kain Tenun Songket Melayu Riau,” 2021.
[2] E. T. N. & Andri, “Penerapan Data Mining Untuk Analisis Daftar Pembelian Konsumen Dengan Menggunakan Algoritma Apriori Pada Transaksi Penjualan Toko Bangunan MDN,” J. Nas. Ilmu Komput., vol. 2, no. 2, pp. 89–101, 2021.
[3] J. Choi, Y. Won, and J. Kim, “Association Rule Mining with Apriori Algorithm for Pediatric Foot Disorders,” WSEAS Trans. Comput., vol. 21, pp. 66–70, 2022, doi: 10.37394/23205.2022.21.9.
[4] M. H. Santoso, “Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom,” Brill. Res. Artif. Intell., vol. 1, no. 2, pp. 54–66, 2021.
[5] U. Baetulloh et al., “Penerapan Metode Association Rule Mining pada Data Transaksi Penjualan Produk Kartu Perdana Kuota Internet,” J. SIMETRIS, vol. 10, no. 1, pp. 173–188, 2019.
[6] F. Afif, R. Swedia, and M. Cahyanti, “Implementasi Algoritma Association Rule untuk Promosi Produk Berbasis Website pada Bengkel Delta Jaya Motor,” J. Ilm. Teknol. dan Rekayasa Vol., vol. 24, no. 2, pp. 152–160, 2019.
[7] D. Rusdianto et al., “Implementasi Data Mining Menggunakan Algoritma Apriori untuk Mengetahui Pola Peminjaman Buku di Perpustakaan Universitas,” J. Sist. Informasi, J-SIKA, vol. 02, no. 2, pp. 1–10, 2020.
[8] N. Ghafoor and M. Ahmad, “Prioritizing Effectiveness of Algorithms of Association Rule Mining,” J. Comput. Learn. Strateg. Pract., vol. 1, no. I, pp. 18–30, 2021.
[9] R. Abd and A. Shekan, “Data Mining and Knowledge Discovery for Big Data in Cloud Environment,” Webology, vol. 18, pp. 1118–1131, 2021, doi: 10.14704/WEB/V18SI04/WEB18186.
[10] H. B. Sabila, “Implementation of Apriori Algorithm for Data Mining on Sales Transaction Data,” Int. J. Electr. Energy Power Syst. Eng., vol. 6, no. 3, pp. 189–193, 2023.
[11] J. Gao, Y. Zhao, L. Li, and S. Deebhijarn, “Feasibility Analysis Model of Transformation from Real Rule Algorithm,” Hindawi Mob. Inf. Syst., vol. 2021, pp. 1–8, 2021.
[12] S. Defit, F. Riandari, and B. Sinaga, “Timeline of Exploration on the Best Response Time in WhatsApp Group " Our Jokes at STMIK Amik ",” Matrik J. Manajemen, Tek. Inform. dan Rekayasa, vol. 20, no. 2, p. 317~324, 2021, doi: 10.30812/matrik.v20i2.1149.