Implemetasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Pola Penjualan Carton Box
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Nila Hardi
*
Universitas Bina Sarana Informatika, Jakarta,
Indonesia
- Jordy Lasmana Putra Universitas Nusa Mandiri, Jakarta, Indonesia
- Tika Adilah M Universitas Bina Sarana Informatika, Jakarta, Indonesia (*) Corresponding Author
Abstract
Good company managers must be able to examine the sales patterns that exist in the company. Some companies have shortcomings, including the problem of stock of goods that do not match the number of goods sold. This certainly affects the level of sales. The existence of sales activities every day, sales transaction data will continue to grow, causing greater data storage. Sales transaction data is only used as an archive without being put to good use. Basically the data set has very useful information. In data mining there are several algorithms or methods that can be done, one of which is the a priori algorithm which is included in the association rules in data mining. A priori algorithm which aims to find frequent item sets in a set of data. A priori algorithm is defined a process to find a priori rules that meet the minimum requirements for support and the minimum requirements for confidence. Test results with a priori algorithm and the system built shows the results which has fulfilled the need to determine sales patterns based on the number of transactions of goods sold. This shows the effectiveness of information from the system about determining the pattern of selling carton boxes to manage stock properly in accordance with the goods with the highest number of transactions seen from 2 caton box sets.
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