PERANCANGAN SKEMA PENJUALAN BARANG RITEL SECARA CROSS SELLING MENGGUNAKAN MARKET BASKET ANALYSIS

Abstract

Abstrak
Pemanfaatan teknologi infomasi dan komunikasi dalam kegiatan transaksi ritel belum disertai dengan pemahaman pentingnya peranan data dalam pengambilan keputusan. Akibatnya, keberadaan data dianggap tidak penting dan menghabiskan storage. Kondisi tersebut mengakibatkan ketidaktepatan dalam penetapan strategi penjualan yang akan berdampak pada kondisi keuangan perusahaan. Strategi penjulan barang secara cross selling merupakan salah satu strategi penjualan yang mampu menaikkan omzet. Cara yang tepat untuk menentukan strategi tersebut adalah dengan melakukan proses data mining terhadap data transaksi penjualan. Melalui kegiatan tersebut akan diperoleh buying habit konsumen dan menjadi rujukan dalam penentuan skema cross selling. Penelitian dilakukan pada data transaksi penjualan ritel dalam periode enam bulan, tujuannya membantu memperoleh skema cross selling. Hasil penelitian dapat menjadi rujukan pihak ritel menentukan pola promosi secara tepat.
Penelitian dilakukan dengan menggunakan metode Knowledge Discovery Database (KDD) dengan teknik market basket analysis dan algoritma apriori. Penelitian dilakukan terhadap data transaksi selama periode 6 bulan dengan nilai support 40% dan 50% , lift ratio di atas 1% dan nilai confidence 75%. Pemilihan nilai-nilai tersebut dimaksudkan agar rule yang dihasilkan memiliki tingkat validitas dan reliabilitas yang tinggi. Hasil penelitian berbentuk pola belanja barang secara berpasangan. Selanjutnya pola yang dihasilkan dijadikan rujukan ritel dalam menentukan strategi penjualan secara cross selling.
Kata kunci :
Data mining, Market basket analysis, cross selling, algoritma apriori
Abstract
The use of information and communication technology in retail transaction activities has not yet been accompanied by an
understanding of the importance of the role of data in decision making. As a result, the presence of data is considered not important and consumes storage. These conditions lead to inaccuracies in the determination of sales strategies that will have an impact on the company's financial condition. Cross selling selling strategy is one sales strategy that is able to increase turnover. The right way to determine this strategy is to conduct data mining processes on sales transaction data. Through these activities, buying habits will be obtained by consumers and become a reference in determining the cross selling scheme. The study was conducted on data on retail sales transactions in a period of six months, the aim of which was to help obtain a cross selling scheme. The results of the research can be a reference for retail parties to determine the exact pattern of promotion.
The study was conducted using the Knowledge Discovery Database (KDD) method with market basket analysis techniques and a priori algorithms. The study was conducted on transaction data for a period of 6 months with support values of 40% and 50%, lift ratio above 1% and a confidence value of 75%. The selection of these values is intended so that the resulting rule has a high level of validity and reliability. The results of the study are in the form of shopping patterns in pairs. Furthermore, the resulting pattern is used as a retail reference in determining cross selling selling strategies.
Keywords :
Data mining, Market basket analysis, cross selling, algoritma apriori