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The purpose of research is to analyze the formation of the target market customer segmentation based on job characteristics, income, education, age, region of origin, and patterns of credit card merchant. The data were analyzed using two data mining techniques of clustering with K-Means and Association Rule Mining (ARM) and supported by Apriori and Random Sampling technique with the Slovin formula and Principal Component Analysis (PCA). The clustering tests in 10 replications on the sampling of 350 clients supported by PCA produced the three best clusters that had the silhouette value close to 1 i.e. 0.39 to 0.40. Meanwhile, the ARM Testing with Apriori using a minimum support of 1% and a minimum confidence of 40% produced two patterns of credit card merchant transactions. In the first pattern, when the Hotel merchant type (hhl) was transacted, the Restaurant merchant type (RRT) was also transacted, and in the second pattern, if the Service Station merchant type (RSS) was transacted, the Restaurant merchant type (RRT) was also transacted. The three clusters and two types of merchant patterns obtained can generate inputs for the company to identify its potential customers based on the characteristics of the target customers by connecting them to the merchant type pattern frequently used.Keywords: credit card, data mining, clustering, ARM, data stream

Pembentukan Target Pasar Berdasarkan Data Stream Transaksi Kartu Kredit (Clustering dan Association Rule) pada PT Bank Bukopin