EKSPLORASI DATA PENJUALAN DI PT ABP UNTUK MENGUNGKAP FAKTA-FAKTA SEGMENTASI KONSUMEN PROPERTI MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING - Stefanus Stefanus; Darmansah Darmansah (Computer Science and Industrial Engineering, 2026)
Housing sales data in property companies are commonly utilized only for administrative purposes, limiting their potential to generate strategic insights into consumer behavior and purchasing patterns. PT Adi Bintan Permata possesses a substantial volume of housing sales transaction data, yet this data has not been systematically analyzed to support data-driven marketing decisions. This study aims to identify consumer segmentation in the property sector at PT Adi Bintan Permata by applying the K-Means clustering algorithm. A quantitative descriptive research approach was employed using housing sales data from January 2022 to June 2025. The variables analyzed include housing selling price, housing unit type, and payment method. Data processing followed the Knowledge Discovery in Databases (KDD) framework, while the optimal number of clusters was determined using the Elbow Method and Silhouette Coefficient. The clustering process was conducted using RapidMiner and Google Colaboratory. The results reveal three distinct consumer clusters with different purchasing characteristics. These findings indicate that K-Means clustering is effective for property consumer segmentation and provides meaningful insights to support more targeted and effective marketing strategies.