Penerapan Algoritma K-Means Clustering Untuk Harga Rumah di Jakarta Selatan - Nuraeni Septiani; Saeful Anwar; Ruli Herdiana (Trending: Jurnal Manajemen dan Ekonomi, 2023)
The price of a house is the value or cost assigned to a residential property, usually expressed in a certain currency. Home prices are determined by various factors, such as location, size, condition, facilities owned, as well as market factors such as demand and competition. House prices are a useful tool for analyzing and understanding the housing market in a given area by displaying information about house prices in that area in an organized and easy-to-read format. The k-means clustering algorithm is used to classify house price data based on features such as location, size, type of house, and so on. The purpose of using the k-means clustering algorithm is to find out the price differences between groups and determine the appropriate price for each group. The results of this analysis can be used to assist in the decision-making process in industrial property, including marketing, determining the selling price, and property development. The data collection technique that the researcher chose used secondary data techniques, which are sources of research data that can produce a number of collected data. during research conducted quickly and informally through the use of intermediary media. The research source that the researchers chose came from Kaggle, with a total of 1010 data on house prices. Based on the research results, it can be concluded that house prices in South Jakarta can be grouped into 10 clusters according to the best dbi value that is 0.129.