ANALISIS POLA PEMBELIAN PRODUK MENGGUNAKAN ALGORITMA APRIORI PADA DATA TRANSAKSI RETAIL

Romadona

doi:10.46880/mtk.v12i2.5730

Authors

Romadona Universitas Duta Bangsa Surakarta

DOI:

https://doi.org/10.46880/mtk.v12i2.5730

Keywords:

Apriori Algorithm, Association Rule, Market Basket Analysis, Retail Transaction, Data Mining

Abstract

The growth of transaction data in the retail sector increases the need for analytical methods capable of identifying consumer purchasing patterns efficiently. This study applies the Apriori algorithm within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework to perform market basket analysis on retail transaction data. The dataset consists of 934,974 transaction records, including 407,171 unique transactions and 27,344 unique products collected between July 2021 and September 2025. After the data cleaning process, 189,724 valid transactions were obtained. To improve computational efficiency, the analysis was limited to the 300 best-selling products, resulting in 90,718 transactions for the modeling stage. Frequent itemset generation was performed using a minimum support value of 0.1% and a maximum itemset length of three, producing 570 frequent itemsets consisting of 300 1-itemsets, 213 2-itemsets, and 57 3-itemsets. Association rule generation using a minimum confidence threshold of 70% and a lift ratio greater than 1 produced 52 valid rules. The best rule achieved a lift ratio of 161.32 and a confidence value of 93.88%, indicating a strong purchasing relationship among school supply products. The results demonstrate that the selected support and confidence parameters are effective in identifying meaningful purchasing patterns. Furthermore, the resulting association rules can support practical retail strategies, including product bundling, shelf arrangement optimization, and inventory management.

References

H. D. Oktory and T. Y. Hadiwandra, “Penerapan Algoritma Apriori untuk Penentuan Pola Pembelian Kacamata pada Optik Indah Optikal,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 4, pp. 1275–1281, Jul. 2024, doi: 10.57152/malcom.v4i4.1353.

I. Fajri, A. Faqih, and S. E. Permana, “PEMANFAATAN ALGORITMA APRIORI DALAM MENGUNGKAP POLA TRANSAKSI UNTUK DESAIN TATA LETAK PRODUK,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 2, Apr. 2025, doi: 10.23960/jitet.v13i2.6181.

I. A. Ashari, A. Wirasto, D. Nugroho Triwibowo, and P. Purwono, “Implementasi Market Basket Analysis dengan Algoritma Apriori untuk Analisis Pendapatan Usaha Retail,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 701–709, Jul. 2022, doi: 10.30812/matrik.v21i3.1439.

K. Brighton and S. Hariyanto, “Penerapan Metode Market Basket Analisis Dengan Algoritma Apriori Pada Toko Ritel Elektronik,” bit-Tech, vol. 7, no. 1, pp. 37–46, Aug. 2024, doi: 10.32877/bt.v7i1.1417.

A. Nur Rahmi and Yosaphat Ananda Mikola, “IMPLEMENTASI ALGORITMA APRIORI UNTUK MENENTUKAN POLA PEMBELIAN PADA CUSTOMER (STUDI KASUS : TOKO BAKOEL SEMBAKO),” Information System Journal, vol. 4, no. 1, pp. 14–19, May 2021, doi: 10.24076/infosjournal.2021v4i1.561.

I. T. Utami and R. Dapa, “Aplikasi Metode Market Basket Analysis dengan Algoritma Apriori untuk Mengetahui Pola Belanja Konsumen pada Online Shop Amerta Fashion,” Indonesian Journal of Applied Statistics, vol. 6, no. 1, p. 96, Jan. 2024, doi: 10.13057/ijas.v6i1.79963.

Andy Hermawan, Bayu Wicaksono, Tigfhar Ahmadjayadi, Bagas Surya Prakasa, and Jasico Dacomoro Aruan, “Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket,” Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa, vol. 2, no. 5, pp. 95–105, Jun. 2024, doi: 10.62383/algoritma.v2i5.137.

F. R. Pare, O. Wati, L. P. Taran, and L. M. Arsai, “Penerapan Data Mining Pada Transaksi Penjualan Barang Menggunakan Metode Apriori (Studi Kasus:Toko BE-MART),” G-Tech: Jurnal Teknologi Terapan, vol. 7, no. 1, pp. 255–261, Jan. 2023, doi: 10.33379/gtech.v7i1.1951.

K. Nisa, “Penerapan Data Mining Terhadap Data Transaksi Sebagai Pendukung Informasi Strategi Penjualan Menggunakan Algoritma Apriori,” Jurnal Teknik Informatika UNIKA Santo Thomas, pp. 306–315, Dec. 2021, doi: 10.54367/jtiust.v6i2.1454.

S. S. Hartinah and Sugiyono, “Pemodelan Data Mining Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus: Kedai Ngodeng & Smoothies),” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 5, no. 3, pp. 3080–3098, Sep. 2024, doi: 10.35870/jimik.v5i3.992.

M. G. Saparudin and S. Sholihin, “Penggunaan Data Mining untuk Analisis Pola Pembelian Pelanggan Menggunakan Metode Association Rule Algoritma Apriori (Studi Kasus di Toko Waspada),” Jurnal Teknologi Sistem Informasi dan Aplikasi, vol. 6, no. 1, pp. 27–33, Jan. 2023, doi: 10.32493/jtsi.v6i1.26927.

Andi Diah Kuswanto, Achmad Rizqullah Blessar, Abdul Goni, Arya Nibras Nayottama Sidiki, Oke Rizki Abdullah Haryu, and Hafid Anhar Hamiki, “Penerapan Algoritma Apriori Dalam Analisis Keranjang Belanja Retail Di Wilayah Jawa Barat,” Saturnus : Jurnal Teknologi dan Sistem Informasi, vol. 2, no. 3, pp. 139–150, Jul. 2024, doi: 10.61132/saturnus.v2i3.208.

M. Wijaya, I. Pradesan, and A. Yulianto, “Implementasi Pola Pembelian Konsumen Menggunakan Metode Association Rule Mining,” Jurnal Teknologi Sistem Informasi, vol. 6, no. 2, pp. 348–358, Sep. 2025, doi: 10.35957/jtsi.v6i2.13405.

K. D. Fernanda, A. P. Widodo, and J. Lemantara, “Analysis and Implementation of the Apriori Algorithm for Strategies to Increase Sales at Sakinah Mart,” JUITA : Jurnal Informatika, vol. 11, no. 2, p. 203, Nov. 2023, doi: 10.30595/juita.v11i2.17341.

A. N. Fuadi, M. H. Bhakti, and A. Premana, “ANALISIS POLA PEMBELIAN KONSUMEN DI TOKO RITEL DMART MENGGUNAKAN ALGORITMA APRIORI BERBASIS WEBSITE,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, Aug. 2024, doi: 10.23960/jitet.v12i3.4963.