Pengembangan Sistem Prediksi Harga Pasar Properti Menggunakan Big Data Platform
DOI:
https://doi.org/10.61769/telematika.v13i1.257Keywords:
analisis data, property, prediksi, regresi linear, NJOPAbstract
Property is a industry that will grow. Some of the problems in property sale and purchase transactions are lack of information about fair sale prices. This price information may be above or below market prices. This study will develop a model for predicting the market price of property in an area. The premise of this research is that the selling price of a house is proportional to the land area and building area in a particular area. The datasets used from this study come from information on selling prices and building area prices from several websites. The approach used in research is using regression and modification. The results of this study are expected to produce a model of recommended property market prices in an area.
Industri properti adalah salah satu industri yang diprediksi akan terus berkembang. Beberapa permasalahan yang muncul dalam transaksi jual-beli properti adalah sulitnya mendapatkan informasi mengenai harga jual properti yang wajar. Informasi harga tersebut bisa jadi berada di atas atau bawah harga pasaran. Penelitian ini akan mengembangkan model prediksi harga pasaran properti di suatu daerah. Premis dari penelitian ini adalah harga jual rumah sebanding dengan luas tanah dan luas bangunan di daerah tertentu. Dataset yang digunakan dari penelitian ini berasal dari informasi harga jual dan harga luas bangunan yang ada dari beberapa website. Pendekatan yang digunakan dalam penelitian adalah menggunakan regresi dan modifikasinya. Hasil dari penelitian ini diharapkan dapat menghasilkan sebuah model rekomendasi harga pasaran properti pada suatu daerah.
References
F. Azkia (2016, Februari 15). Apa itu NJOP dan NJKP [Online].
Tersedia: https://www.rumah.com/berita-properti/2016/2/117447/apa-itu-njop-dan-njkp.
A. Bhatia, Big Data Analytics, MU CBGS, 2016.
W. Brand, Big Data for Dummies, First Edition, New Jersey: John Wiley & Sons, Inc, 2013.
W. Ardiyanto (2017, Mei 21). Mau Survei Properti? Pahami Peran Semua Pihak Yang Terlibat! [Online]. Tersedia: https://www.rumah.com/berita-properti/2017/5/152675/mau-survei-properti-pahami-peran-semua-pihak-yang-terlibat.
T. Kwaiec. The Amazon Recommendations Secret to Selling More Online [Online]. Avaliable: http://rejoiner.com/resources/amazon-recommendations-secret-selling-online/
G. Linden, B. Smith, J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, IEEE Internet Computing, Vol. 7, pp. 76-80, January 2003.
B. Marr (2015, April 22). Big Data: How Netflix Uses It to Derive
Business Success [Online]. Avaliable: https://www.smartdatacollective.com/big-data-how-netflix-uses-it-drive-business-success/.
Z. Bulygo. How Netflix Uses Analytics To Select Movies, Create Content, and Make Multimillion Dollar Decisions [Online]. Avaliable: https://blog.kissmetrics.com/how-netflix-uses-analytics/
I. Kurniawan, Alasan Mengapa Transaksi Jual Beli Properti Belum Bisa Online, [Online]. Tersedia: https://id.techinasia.com/alasan-mengapa-transaksi-jual-beli-properti-belum-bisa-online
Y. Putri, Segudang Masalah Konsumen Perumahan [Online], Tersedia: http://ylki.or.id/2011/10/segudang-masalah-konsumen-perumahan/
J. Yang, “Housing Price Prediction Using Support Vector Regression,” Master’s Projects, Mei 2017.
V. Hugo, “Property valuation using machine learning algorithms a study in a metropolitan - area of Chile,” Conference: AMSE Conference Santiago/Chile, Januari 2016.
Aaron Ng, “Machine learning for a London housing price prediction mobile application,” Final Project, Department of Computing, Imperial College London, Juni 2015.
A. Seutin, Using Machine Learning to Predict Housing Price Given Multivariate Input, 2016.
V. Venkat, S. Vijay, S. Banu, “Identifying Customer Interest in Real Estate Using Data Mining Techniques,” International Journal of Computer Science and Information Technologies, Vol. 5, No. 3, pp. 3081-3084, 2014.
A. Nur, R. Ema, H. Taufiz, W. Firdaus, “Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization (Case Study: Malang, Indonesia),” International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10, pp. 323-326, 2017.
V. J. L. Engel and S. Suakanto, “Model Inferensi Konteks Internet of Things pada Sistem Pertanian Cerdas,” Jurnal Telematika, vol. 11, no. 2, pp. 49–54, 2016
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