House Prices Segmentation Using Gaussian Mixture Model-Based
Clustering

House Prices Segmentation Using Gaussian Mixture Model-Based
Clustering

segmentation, clustering, Gaussian Mixture Model

House is a place for humans to live and a main necessity for humans. For years, the need for houses is increasing and varied
so that it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses.
This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering
Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model
so that we can obtain useful information for the potential buyer. Clustering with GMM utilize the log-likelihood function to
optimize the GMM parameters. The result of this research is houses in DKI Jakarta can be segmented into 3 different clusters.
The first cluster is for the low-profile houses. The second cluster is for the mid-profile houses. The third cluster is for the highprofile houses. The silhouette score that was produced by the clustering method is 0.60866 meaning that this score is quite
good because it’s close to a value of 1.

Muhammad Hafidh Raditya1
, Indwiarti2
, Aniq Atiqi Rohmawati3

Telkom University

31-10-2022

Fajar bagus W

PDF

Indonesia

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