Property Business Classification Model Based on Indonesia E-Commerce Data

• URL: http://arxiv.org/abs/2102.12300v1
• Date: Wed, 24 Feb 2021 14:29:34 GMT
• Title: Property Business Classification Model Based on Indonesia E-Commerce Data
• Authors: Andry Alamsyah, Fariz Denada Sudrajat, Herry Irawan
• Abstract summary: Indonesia's e-commerce property business has positive trending shown by the increasing sales of more than 500% from 2011 to 2015. To predict the property sales, this research employed two different classification methods in Data Mining which are Decision Tree and k-NN classification. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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