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All Journal Jurnal Informatika Universitas Pamulang Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Sistem Informasi dan Sistem Komputer JAMAIKA: Jurnal Abdi Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Inspiration: Jurnal Teknologi Informasi dan Komunikasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains Journal of Innovative and Creativity Riau Jurnal Teknik Informatika Indonesian Journal of Infomatics
Kecitaan Harefa
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Transportation Other

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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 Abdullah Abdullah Aenun Nisa Andrian Andrian Ardianto Moenir Arif Cahyo Utomo Cicih Sri Asih Daevi Dandi Ramdani Fikri Miftahul Falah Indrajit Sumarsono Indriyani Octavia Joko Priambodo Kiki Nurdiansyah M. Chandra Nugraha Muhammad Rizki Yusnadi Muhammad Zirlda Prairi Ny.Ayni Suwarni Herry Perani Rosyani Rinna Rachmatika Zurnan Alfian