ANALISIS CLUSTERISASI PENGUNJUNG MALL BERDASARKAN USIA DAN PENDAPATAN MENGGUNAKAN ALGORITMA DBSCAN - Prihatmono, Medy Wisnu (Yayasan Nusantara Hasana Berdikari, 2025)
Data mining can be used to extract valuable insights from irregular and high-volume mall customer data. More effective shopping strategies can be created by thoroughly understanding the demographics of mall customers to attract new customers, increase retention of existing customers, and create a more engaging shopping experience. The purpose of this study was to look at visitor data that has implemented strategies and to increase visitor volume. The research sample consisted of 200 mall visitor respondents who participated in the Ramadan quiz program during the period of 1 to 30 days of the month of Ramadan. Based on the data density analysis, five Cluster groups were formed with the following distribution: Noise (Cluster -1): 130 data (65% of the total sample), representing data points that are not associated with significant density, Cluster 0: 10 data (5% of the total sample), Cluster 1: 14 data (7% of the total sample), Cluster 2: 11 data (5.5% of the total sample), Cluster 3: 35 data (17.5% of the total sample).