Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location. This research aims to predict house prices based on NJOP houses in Malang city with regression analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction. The result from this research proved combination regression and PSO is suitable and get the minimum prediction error obtained which is IDR 14.186.

@article{Alfiyatin2017,
  title     = {Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {10},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Adyan Nur Alfiyatin and Ruth Ema Febrita and Hilman Taufiq and Wayan Firdaus Mahmudy},
  doi       = {10.14569/IJACSA.2017.081042},
  url       = {https://doi.org/10.14569/IJACSA.2017.081042}
}