AN ANALYSIS OF THE CORRELATION OF CONSUMER PURCHASE PATTERNS USING APRIOPRI METHOD (A CASE STUDY OF CV. VIFOR CIPTA SOLUSI)

Junianto Bagas Prasetyo, Mutaqin Akbar

Abstract

CV. Vifor Cipta Solusi is a company that provides electronic equipment. The competition in business, especially among companies providing electronic equipment, is growing. To increase sales of the products, business people in this field need to set a strategy. This research applied the data mining with association technique to find out strong combinations between items. Apriori algorithm is part of the association method in data mining which aims to find out frequent item sets from a certain set of data. The apriori algorithm process is carried out by determining frequent itemsets that meet the requirements of the predetermined minimum support and minimum confidence. From these two measurements, the final association rule can be obtained by which the value of the accuracy (validity) of the association is calculated using the lift ratio. This research obtained 2 association rules with item attributes totaling 294 types of products from 20 main attributes with 1813 records processed from 5282 raw transaction data from January to December 2019. The strong rules obtained are MagnetToner HP Laserjet 85 A, Drum Toner HP Laserjet 85 A => HP Laserjet 85 A toner with a support value of 3.42%, and a confidence value of 81.58% with a lift ratio of 5.00.If a consumer buys an HP Laserjet 85 A MagnetToner and a HP Laserjet 85 A Toner Drum, the consumer will also buy an HP Laserjet 85 A Toner.

 

Keywords

data mining;apriori algorithm;electronic stores

References

Ristianingrum, R., & Sulastri, S. (n.d.). IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI. SINTAK 2017, 372–382.

Aditya, A., Marisa, F., & Purnomo, D. (2016). Penerapan Algoritma Apriori Terhadap Data Penjualan di Toko Gudang BM. Journal of Information Technology and Computer Science, 1(1), 1–5.

Fajar Rodiansyah, S. (2015). Algoritma Apriori untuk Analisis Keranjang Belanja pada Data Transaksi Penjualan. Infotech Journal, 1(2), 36–39.

Rahmawati, F., & Merlina, N. (2018). Metode Data Mining Terhadap Data Penjualan Sparepart Mesin Fotocopy Menggunakan Algoritma Apriori. Jurnal Penelitian Ilmu Komputer, System Embedded & Logic, 6(1), 9–20.

Tana, M. P., Marisa, F., & Wijaya, I. D. (2018). Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Pada Toko Oase Menggunakan Algoritma Apriori. Jurnal Informatika Merdeka Pasuruan, 3(2), 17–22.

Yanto, R., & Khoiriah, R. (2015). Implementasi Data Mining dengan Metode Algoritma Apriori dalam Menentukan Pola Pembelian Obat. Citec Journal, 2(2), 102–113.

Simbolon, P. H. (2019). Implementasi Data Mining Pada Sistem Persediaan Barang Menggunakan Algoritma Apriori (Studi Kasus: Srikandi Cash Credit Elektronic dan Furniture). Jurnal Riset Komputer, 6(4), 401–406.

Tampubolon, K., Saragih, H., & Reza, B. (2013). Implementasi data mining algoritma apriori pada sistem persediaan alat-alat kesehatan. Informasi Dan Teknologi Ilmiah, 1(1).

Han, J., Pei, J., Kamber, M., & Safari, an O. M. C. (2011). Data Mining: Concepts and Techniques, 3rd Edition.

Santoso, L. W. (2003). Pembuatan Perangkat Lunak Data Mining Untuk Penggalian Kaidah Asosiasi Menggunakan Metode. Jurnal Informatika, 4(2), 49–56.

Listriani, D., Setyaningrum, A. H., & Eka, F. (2018). Penerapan Metode Assosiasi Menggunakan Algoritma Pada Aplikasi Analisa Pola Belanja Konsumen (Studi Kasus Toko Buku Gramedia Bintaro). Jurnal Teknik Informatika, 9(2). https://doi.org/10.15408/jti.v9i2.5602

Article Metrics

Abstract view: 222 times
Download     : 60   times

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

  • There are currently no refbacks.