Penerapan Data Mining Untuk Menentukan Segmentasi Pelanggan Dengan Menggunakan Algoritma K-Means dan Model RFM Pada E-Commerce - Sharyanto Sharyanto; Dwi Lestari (STMIK Budi Darma, 2022)
The growth of technology has a big impact on life, one of which is the use of the internet. In Indonesia, internet usage has increased every year, this has an impact on changes in the buying and selling process carried out by the community. The basic reason caused by these changes is a wider market reach. So it can be said that the presence of the online market is a positive impact caused by the development of these technologies. E-Commerce is a process of buying and selling transactions and also leasing services that are carried out by utilizing internet technology that is already available on certain media. On this basis, business actors in E-Commerce should have their own characteristics that are easily recognized by consumers. It is necessary for business people to segment potential customers. However, the problem of customer segmentation has become a problem in itself for business actors, this is due to the lack of knowledge of how to segment the customers. Data mining is a process that can be done to solve problems. With data mining the process is carried out by processing the dataset and generating new information. In data mining itself, there are several completion processes, namely the K-Means algorithm for the clustering process and the RFM model for segmentation analysis. The results obtained are that there are 4 customer segments. This can be seen from the testing process carried out from K = 2 to K = 5, in the K = 4 test the most optimal BDI value is 0.6788