House Prices Prediction : Multiple Linear Regression vs Ridge vs Polynomial - JASMAN PARDEDE; RAYYAN RAYYAN (Institut Teknologi Nasional, Bandung, 2023)
AbstractThe phenomenon of falling or rising house prices has attracted the interest of researchers as well as many other interested parties. The house not only be used as a place to live, it is also used as an investment instrument. Errors in determining the price of the house can result in losses. However, with data from developers, machine learning models can be applied for price predictive analysis. Several methods are used such as multiple linear regression, ridge, and polynomial. Model performance was measured using evaluation matrices such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and also Mean Absolute Error (MAE). Multiple linear regression models yielding values of 0.0952, 0.3086, 0.2452, ridge yielding values of 0.0952, 0.3086, 0.2453, and polynomial yielding values of 0.0874, 0.2955, 0.2344. These results prove that the polynomial regression model with a value of degree = 2, coupled with a regularization technique using ridge regression with a value of Alpha = 100 can produces the best performance judging from the value of the error matrix it produces, the model will also be used to predict house prices in a web-based applications.Keywords: Multiple Linear Regression, Ridge Regression, Polynomial RegressionAbstrakFenomena turun atau naiknya harga rumah telah menarik minat dari peneliti juga banyak pihak lain yang berkepentingan. Rumah tidak hanya dijadikan sebagai tempat tinggal, rumah juga digunakan sebagai instrumen investasi. Kesalahan menentukan harga rumah dapat mengakibatkan kerugian. Namun, dengan adanya data – data dari pengembang, pembuatan model machine learning dapat diaplikasikan guna keperluan analisis prediktif harga. Beberapa metode yang digunakan seperti regresi linear berganda, ridge, dan polinomial. Performa model diukur menggunakan matriks evaluasi seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan juga Mean Absolute Error (MAE). Model regresi linear berganda menghasilkan nilai 0.0952, 0.3086, 0.2452, ridge menghasilkan nilai 0.0952, 0.3086, 0.2453, dan polinomial menghasilkan nilai 0.0874, 0.2955, 0.2344. Hasil tersebut membuktikan bahwa model regresi polinomial dengan nilai degree = 2, ditambah dengan teknik regularisasi regresi ridge dengan nilai Alpha = 100 dapat menghasilkan performansi terbaik dilhat dari nilai matriks error yang dihasilkannya, model tersebut juga akan digunakan untuk melakukan prediksi harga rumah pada aplikasi yang berbasis website.Kata kunci: Regresi Linear Berganda, Regresi Ridge, Regresi Polinomial