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Lukas Umbu Zogara
Universitas Utpadaka Swastika
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Machine Learning for Predicting Property Purchase Behavior: A Systematic Literature Review Lukas Umbu Zogara; Asep Surahmat
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.329

Abstract

This study aims to examine the application of machine learning algorithms in predicting property purchase behavior based on consumer data. The main problem addressed is the limited use of intelligent data analysis in understanding consumer behavior in the Indonesian property sector, despite increasing market data availability. This research employs a systematic literature review approach by analyzing studies published in the last five years, focusing on classification algorithms such as Decision Tree, Random Forest, and Support Vector Machine (SVM). The analysis includes data collection, evaluation, and synthesis of selected studies. The results indicate that algorithm performance varies depending on data characteristics and application context. Random Forest tends to show strong performance in terms of accuracy and robustness, while Decision Tree and SVM also demonstrate competitive results in certain scenarios. These findings reflect general trends rather than definitive conclusions. Key factors influencing property purchase decisions include location, price, and developer reputation. In conclusion, machine learning has significant potential to support data-driven decision-making in the property sector. Future research should integrate real-time and more diverse data to improve predictive model accuracy
Co-Authors Adiiba Dwikanthi Ahmad Sururi Alfaujianto, Moh Alfaujianto, Moh. Ameliana Ameliana Dai Payon Binti Gabriel, Cecilia Darul Mukminin Dhimas Tribuana Fahmi Rizky Nugraha Fajar Muttaqi Fajar Muttaqi Grace Limiko Hasyanta, Gopas Leny Tritanto Ningrum Marvil Worotitjan Maulana Agung Saputro Moh Alfaujianto Moh. Alfaujianto Muttaqi, Fajar Nikson Marpaung Ningrum, Leny Pupista, Orinda Rindi Widya Yato, Dhimas Buing Rusdah Rusdah Sari, Santi Purnama Suharmat, Asep Surahmat, Asep Sutrisno, Gono Syukron Makmun Tb. Yudi Muhtadi Tehupuring, Ronald Tobing, Suasana Hisanty Umiyati, Hesti Yato, Dhimas Buing Rindi Widra Yupiter Halawa