  Online property business or known as e-commerce is currently experiencing an
increase in home sales. Indonesia's e-commerce property business has positive
trending shown by the increasing sales of more than 500% from 2011 to 2015. A
prediction of the property price is important to help investors or the public
to have accurate information before buying property. One of the methods for
prediction is a classification based on several distinctive property industry
attributes, such as building size, land size, number of rooms, and location.
Today, data is easily obtained, there are many open data from E-commerce sites.
E-commerce contains information about homes and other properties advertised to
sell. People also regularly visit the site to find the right property or to
sell the property using price information which collectively available as open
data. To predict the property sales, this research employed two different
classification methods in Data Mining which are Decision Tree and k-NN
classification. We compare which model classification is better to predict
property price and their attributes. We use Indonesia's biggest property-based
e-commerce site Rumah123.com as our open data source, and choose location
Bandung in our experiment. The accuracy result of the decision tree is 75% and
KNN is 71%, other than that k-NN can explore more data patterns than the
Decision Tree.
