Implementasi Algoritma Linear Regression Untuk Prediksi Harga Rumah di Daerah Tebet - Aditya Pratama; Adha Maulana; Rendy Amy Saputra (Institut Shanti Bhuana, 2025)
This research focuses on the issue of house price variation in the Tebet area, South Jakarta, influenced by factors such as land area, building area, number of bedrooms, number of bathrooms, and additional facilities. This price variability makes it difficult for potential buyers and sellers to determine a fair price, potentially resulting in poor decisions and financial losses. Therefore, the main objective of this research is to develop a house price prediction model using the linear regression algorithm, which is expected to provide a more accurate price estimate based on relevant features.as a solution, this research applies the linear regression algorithm to analyze and predict house prices, providing useful information for stakeholders, including buyers, sellers, and real estate agents. The methods used include collecting house price data and related features from the Rumah123 property platform, which are then pre-processed by dividing them into training data (80%) and test data (20%). The model is evaluated using the Mean Squared Error (MSE) and R-squared metrics. The research results show that this model has an R-squared of 0.7713, which means it can explain about 77% of the variation in house prices. This model also predicts the price of a new house with certain features, such as a building area of 100 m² and a land area of 300 m², at around IDR 8,555,000,000. This research is expected to make a significant contribution to understanding the dynamics of house prices in Tebet.