Clustering of potencial customer in digital trade transaction

The growth of digital trade transaction data in Indonesia is very rapid. Retail ecommerce in Indonesia is currently growing rapidly although it is still smaller than other countries, growing by around 30% every year for the last five years. Customer grouping is carried out based on different types of customer data, including customer value data, customer profile data, customer transaction data, and customer purchase order data. In the following sections, we briefly introduce each type. This study used data mining that was collected from potential customers by online questionnaires. The customer set data used were 309 respondents. The data collection that the authors do it by distributing questionnaires online through Google form. Clustering process used neural network algorithm. The competitive network used data clustering. The research result based on the results of data collection, the data grouping can be obtained with cluster 3 epoches 1000 data with kohonen parameter 0.01 and cluster 3 epoches 1000 data with kohonen parameter 0.01.