# RECORD 8392a5c77de21a6f7d3fc479d14609916a0ee42e # TITLE: Penerapan Metode Linear Regression Untuk Memprediksi Harga Rumah # YEAR: 2026.0 VENUE: JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi DOI: 10.67763/jitsi.v1i2.66 # AUTHORS: Muhammad Arizal Dwisakti; Muhammad Ridho Pramana; Riski Juliandri; Taronisokhi Zebua # PAGES: 6 CANDIDATE TABLE PAGES (1-based): [] # ABSTRACT: House prices are an important indicator in the property sector and are influenced by various factors, including physical characteristics of the building and location. This study aims to analyze and predict house prices using a linear regression method by utilizing several variables, namely land area, building area, number of bedrooms, number of bathrooms, parking availability, and distance to the city center. The data used in this study are secondary data collected through a web scraping process and are focused on houses with a price range of 300700 million rupiah to represent the middle-market segment. The research stages include data preprocessing, Pearson correlation analysis, multicollinearity testing, multiple linear regression modeling, and model performance evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The dataset is divided into 80% training data and 20% testing data. The results show that the constructed linear regression model achieves an R² value of 0.3078, indicating that the independent variables are able to explain 30.78% of the variation in house prices. The RMSE value of 117,482,242 indicates that prediction errors remain relatively high due to the wide variation in house prices. The correlation analysis results reveal that the number of bathrooms and the distance to the city center have a relatively stronger relationship with house prices compared to other variables. This study demonstrates that linear regression can be used as an initial approach for house price prediction; however, it still has limitations in explaining overall price variations. Therefore, future research is expected to improve prediction performance by incorporating additional variables or applying more advanced modeling methods. ===== PAGE 1 (score 16, numeric-paren 0, table-words 2, var-words 12) ===== 59 JITSI: Jurnal Informatika Dan Teknologi Sistem Informasi ISSN : 3123-6650 (Media Online) Vol. 1 No. (Mei, 2026), 59-64 https://jurnal.interaksisaintek.com/index.php/JITSI Penerapan Metode Linear Regression Untuk Memprediksi Harga Rumah Muhammad Arizal Dwisakti 1, Muhammad Ridho Pramana 2, Riski Juliandri 3 Taroni sokhi Zebua4, 1Sistem Informasi, Fakultas : Ilmu Komputer, Universitas Budi darma, Medan, Indonesia muhammadarizaldwisakti@gmail.com, mr8520219@gmail.com, riskizuliandri966@gmail.com, taronizeb@gmail.com Abstract - House prices are an important indicator in the property sector and are influenced by various factors, including physical characteristics of the building and location. This study aims to analyze and predict house prices using a linear regression method by utilizing several variables, namely land area, building area, number of bedrooms, number of bathrooms, parking availability, and distance to the city center. The data used in this study are secondary data collected through a web scraping process and are focused on houses with a price range of 300– 700 million rupiah to represent the middle-market segment. The research stages include data preprocessing, Pearson correlation analysis, multicollinearity testing, multiple linear regression modeling, and model performance evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The dataset is divided into 80% training data and 20% testing data. The results show that the constructed linear regression model achieves an R² value of 0.3078, indicating that the independent variables are able to explain 30.78% of the variation in house prices. The RMSE value of 117,482,242 indicates that prediction errors remain relatively high due to the wide variation in house prices. The correlation analysis results reveal that the number of bathrooms and the distance to the city center have a relatively stronger relationship with house prices compared to other variables. This study demonstrates that linear regression can be used as an initial approach for house price prediction; however, it still has limitations in explaining overall price variations. Therefore, future research is expected to improve prediction performance by incorporating additional variables or applying more advanced modeling methods. Keywords: house price, linear regression, coefficient of determination, RMSE, Pearson correlation. Abstrak - Harga rumah merupakan salah satu indikator penting dalam sektor properti yang dipengaruhi oleh berbagai faktor, baik fisik bangunan maupun lokasi. Penelitian ini bertujuan untuk menganalisis dan memprediksi harga rumah menggunakan metode regresi linear dengan memanfaatkan variabel luas tanah, luas bangunan, jumlah kamar tidur, jumlah kamar mandi, tempat parkir, dan jarak ke pusat kota. Data yang digunakan dalam penelitian ini merupakan data sekunder yang dikumpulkan melalui proses web scraping dan difokuskan pada rumah dengan kisaran harga 100–700 juta rupiah untuk merepresentasikan segmen pasar menengah. Tahapan penelitian meliputi prapemrosesan data, analisis korelasi Pearson, uji multikolinearitas, pemodelan regresi linear berganda, serta evaluasi kinerja model menggunakan koefisien determinasi (R²) dan Root Mean Square Error (RMSE). Data dibagi menjadi data pelatihan sebesar 80% dan data pengujian sebesar 20%. Hasil penelitian menunjukkan bahwa