Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices

Sigit Auliana; Basuki Rakhim Setya Permana; Gagah Dwiki Putra Aryono

doi:10.55681/jige.v5i2.2794

Authors

Sigit Auliana Program Studi Sistem Informasi, Universitas Bina Bangsa, Indonesia
Basuki Rakhim Setya Permana Program Studi Imu Komputer, Universitas Bina Bangsa, Indonesia
Gagah Dwiki Putra Aryono Program Studi Sistem Informasi, Universitas Bina Bangsa, Indonesia

DOI:

https://doi.org/10.55681/jige.v5i2.2794

Keywords:

Multiple - Linear Regression, Random Forest Regression Prediction, House Prices

Abstract

Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.

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