ANALISIS PROPERTI PROSPEK DAN NON-PROSPEK BERDASARKAN DATA IKLAN MENGGUNAKAN METODE K-MEANS DAN K-MEDOIDS - Maulana, Adha; Avianto, Donny (Journal of Data Science Theory and Application, 2025)
The significant growth of the property industry market in the city of Yogyakarta. has attracted more attention from property companies. This is glanced at by property companies in bridging sales. However, property agency companies often experience losses in plotting advertising budgets. With this problem, the author offers a clustering system for prospective and non-prospective property spots using the K-Means and K-Medoids methods with the Forward Selection feature selection method. This study aims to allocate advertising budgets to targeted projects with great potential. The data used is primary data, namely IRSC data from company x in Yogyakarta with a range (January 2023–March 2024) totaling 212 records. Data processing uses the RapidMiner application with a data composition of 70% used for training data and 30% for testing data. This process produces a DBI value of 1.060 for the K-Medoids method without feature selection and 1.974 for the K-Medoids method using feature selection. The best method produced by the K-Means method using and without feature selection with a DBI value of 0.148.