Grouping Mortgage Data By Job Using The Clustering Method

Abstract

This research discusses the application of a cluster-based data grouping method (clustering) to group Home Ownership Credit (KPR) data based on the type of work of the borrowers. The aim of this research is to identify possible patterns in mortgage data and group them into groups that have similar job characteristics. In this study, the cluster method is used to classify mortgage data based on the job attributes of the borrowers. The data collected includes job information and several other related attributes. The clustering process is carried out by applying certain algorithms to group data into different groups. The results of this study are expected to provide insight into the relationship between the type of work and the characteristics of mortgage borrowers. With a better understanding of these patterns, financial institutions and related agencies can make more informed decisions in managing mortgage products, credit risk, and developing more effective marketing strategies. This data grouping method can contribute to improving the efficiency of data analysis and decision making in the financial sector.