Clustering for Searching Type of House Suitable for New Consumer Candidates Using K-Means Clustering Method (case Study of PT. Maxima Jaya Perkasa)

Abstract

For some Indonesian people, housing is one of the secondary needs, so that in choosing the right housing
must be in accordance with the wishes of consumers. With the existence of PT. Maxima Jaya Perkasa, which was
pioneered since 2012, in which the data on housing sales in the company has increased rapidly each year. Then data
mining analysis can be done using the K-means Clustering method. K-means Clustering is a method of clustering nonhierarchical
data which seeks to partition existing data into two or more groups. This method partitioned the data into
groups so that the data with the same characteristics were entered into the same group and the data with different
characteristics were grouped into other groups. This study uses data such as salary income, age, status, house prices
and mortgage payments. The results of this study were conducted twice using 12 training data training data and 100
training data plus 1 as test data and obtained an accuracy value of 83% and error of 17%.