1# RECORD 8392a5c77de21a6f7d3fc479d14609916a0ee42e2# TITLE: Penerapan Metode Linear Regression Untuk Memprediksi Harga Rumah3# YEAR: 2026.0 VENUE: JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi DOI: 10.67763/jitsi.v1i2.664# AUTHORS: Muhammad Arizal Dwisakti; Muhammad Ridho Pramana; Riski Juliandri; Taronisokhi Zebua5# PAGES: 6 CANDIDATE TABLE PAGES (1-based): []6# 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.789===== PAGE 1 (score 16, numeric-paren 0, table-words 2, var-words 12) =====101159 12 13JITSI: Jurnal Informatika Dan Teknologi Sistem Informasi 14ISSN : 3123-6650 (Media Online) 15Vol. 1 No. (Mei, 2026), 59-64 16https://jurnal.interaksisaintek.com/index.php/JITSI 17Penerapan Metode Linear Regression Untuk Memprediksi Harga 18Rumah 19 20Muhammad Arizal Dwisakti 1, Muhammad Ridho Pramana 2, Riski Juliandri 3 Taroni 21sokhi Zebua4, 221Sistem Informasi, Fakultas : Ilmu Komputer, Universitas Budi darma, Medan, 23Indonesia 24muhammadarizaldwisakti@gmail.com, mr8520219@gmail.com, 25riskizuliandri966@gmail.com, taronizeb@gmail.com 26 27Abstract - House prices are an important indicator in the property sector and are influenced by 28various factors, including physical characteristics of the building and location. This study aims 29to analyze and predict house prices using a linear regression method by utilizing several 30variables, namely land area, building area, number of bedrooms, number of bathrooms, parking 31availability, and distance to the city center. The data used in this study are secondary data 32collected through a web scraping process and are focused on houses with a price range of 300–33700 million rupiah to represent the middle-market segment. 34The research stages include data preprocessing, Pearson correlation analysis, 35multicollinearity testing, multiple linear regression modeling, and model performance 36evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). 37The dataset is divided into 80% training data and 20% testing data. The results show that the 38constructed linear regression model achieves an R² value of 0.3078, indicating that the 39independent variables are able to explain 30.78% of the variation in house prices. The RMSE 40value of 117,482,242 indicates that prediction errors remain relatively high due to the wide 41variation in house prices. 42The correlation analysis results reveal that the number of bathrooms and the distance 43to the city center have a relatively stronger relationship with house prices compared to other 44variables. This study demonstrates that linear regression can be used as an initial approach for 45house price prediction; however, it still has limitations in explaining overall price variations. 46Therefore, future research is expected to improve prediction performance by incorporating 47additional variables or applying more advanced modeling methods. 48 49Keywords: house price, linear regression, coefficient of determination, RMSE, Pearson 50correlation. 51 52 53Abstrak - Harga rumah merupakan salah satu indikator penting dalam sektor properti yang 54dipengaruhi oleh berbagai faktor, baik fisik bangunan maupun lokasi. Penelitian ini bertujuan 55untuk menganalisis dan memprediksi harga rumah menggunakan metode regresi linear dengan 56memanfaatkan variabel luas tanah, luas bangunan, jumlah kamar tidur, jumlah kamar mandi, 57tempat parkir, dan jarak ke pusat kota. Data yang digunakan dalam penelitian ini merupakan 58data sekunder yang dikumpulkan melalui proses web scraping dan difokuskan pada rumah 59dengan kisaran harga 100–700 juta rupiah untuk merepresentasikan segmen pasar menengah. 60Tahapan penelitian meliputi prapemrosesan data, analisis korelasi Pearson, uji 61multikolinearitas, pemodelan regresi linear berganda, serta evaluasi kinerja model menggunakan 62koefisien determinasi (R²) dan Root Mean Square Error (RMSE). Data dibagi menjadi data 63pelatihan sebesar 80% dan data pengujian sebesar 20%. Hasil penelitian menunjukkan bahwa 64