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All Journal Jurnal Informatika Universitas Pamulang OKTAL : Jurnal Ilmu Komputer dan Sains Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) APPA : Jurnal Pengabdian Kepada Masyarakat Riau Jurnal Teknik Informatika
Kecitaan Harefa
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Astronomy Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences

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Application of Multiple Linear Regression Algorithm for House Price Estimation Based on Building Location and Area to Improve Predictive Accuracy in Real Estate Valuation Kecitaan Harefa
Riau Jurnal Teknik Informatika Vol. 4 No. 3 (2025): November 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i3.3993

Abstract

Significant differences in home prices, even on properties with similar building sizes and locations, pose a major challenge in accurately determining property valuations. The discrepancy between the actual market price and the estimated value makes it difficult for potential buyers, sellers, and developers to make the right decision. To overcome these problems, this study applied the Multiple Linear Regression (MLR) algorithm in the Decision Support System (DSS) to estimate house prices based on the location and area of the building. The dataset used consists of 545 housing data points with variables such as house prices, locations, and building areas. The research stages include data collection, pre-processing (data cleaning and normalization), model development using MLR, and model performance evaluation. The evaluation was carried out using the division of trained data and test data with an 80:20 ratio, so that the model was tested using data that was not previously trained. The results showed that the model produced a Mean Absolute Error (MAE) of 1,474,748.13, a Root Mean Squared Error (RMSE) of 1,917,103.70, and a coefficient of determination (R²) of 0.273. A relatively low R² value indicates that the location and area variables of the building are not sufficient to explain the overall variation in house prices, so the addition of other variables—such as the number of rooms, facilities, and environmental conditions—is needed to improve the accuracy of the prediction and produce a more representative price estimate.
Co-Authors Aditya Rangga Prasetya Aditya Tri Herdiansyah Afrizal Nur Perdana Aqidatul Izzah Chairul Ari Fachri Zadiq Arisman laia Dony Sapta Nurhayadi Dzaky Azizan Haidar Adlia Hernanda Faris Khoiri Jason Ikbal Khairul Umam Kristiani Apriyanti Ndraha Kurnianto Octian Junaidi Muhamad Fariz Rivai Muhammad Ridwan Ramadhan Oktafpianus Gea Pras Parman Wau Priska Diniati Rebeka Ndaki Sigit Dwiantoro Tian Aldi Kurniawan Wahyu Candra Wijaya Yosef Ibrahim