Normalisasi Data Dengan Menggunakan Model Min Max Untuk Klasifikasi Nasabah Potensial Pada Bidang Pembelian Properti Menggunakan Algoritma K-Nearest Neighbor

 (*)Viola Putri Mail (Universitas Budi Darma, Medan, Indonesia)

(*) Corresponding Author

Abstract

The customer is the most important asset in a company, especially those engaged in the property sector. The customer is also someone who supports the running of a business within a company. Potential customers have one or several definitions that benefit from physical products with various variables used in determining such as price, quality, place and services that can be provided based on the results to achieve a target. Property is synonymous with real estate, houses, land, shop houses, buildings or warehouses. In this era of globalization, competition in the property business is becoming very tight and sharp, both in the national and international markets. Every property company is required to always pay attention to the needs and desires of customers by providing the best service in terms of quality and payment systems, one of which is a gradual cash system. This is what makes it difficult for companies to identify potential customers. With the increasing willingness expected by customers in purchasing property, a classification method is needed that can help determine potential customers in the field of property purchases. Based on this, data mining is considered to be able to help problems. In this research using data normalization using the Min Max model for data mining classification with the K-Nearest Neighbor algorithm method. The results of the classification are used as a decision to acquire potential customers in buying property. In this study, data were collected through direct observation at PT. Amanda Barokah Together. Then the data is analyzed to determine potential customers in property purchases.

Keywords


Potential Customer, Data Normalization; Min Max; K-Nearest Neighbor

Full Text:

PDF


Article Metrics

Abstract view : 0 times
PDF - 5 times

References

B. A. B. Ii and A. T. Pustaka, “Bab Ii_Tony Hartono Aji_Manajemen’14,” pp. 9–40, 1987.

S. Dwiasnati and Y. Devianto, “Optimasi Prediksi Keputusan Calon Nasabah Potensial Berbasis Particle Swarm Optimization,” Optimasi Prediksi Keputusan Calon Nasabah Potensial Berbas. Part. Swa, vol. 6, no. 2, pp. 286–292, 2019.

D. Yunita, “Perbandingan Algoritma K-Nearest Neighbor dan Decision Tree untuk Penentuan Risiko Kredit Kepemilikan Mobil,” J. Inform. Univ. Pamulang, vol. 2, no. 2, p. 103, 2017, doi: 10.32493/informatika.v2i2.1512.

H. Annur and M. E. Lasulika, “Klasifikasi Nasabah Kredit Koperasi Menggunakan Algoritma K-Nearest Neighbor,” J. Inform. Upgris, vol. 5, no. 2, pp. 126–129, 2019.

Irfan Mahendra and Putri Buana, “Menentukan Source Terbaik Untuk Menemukan Pelanggan Potensial Menggunakan Algoritma K-Nearest Neighbor,” JITSI J. Ilm. Teknol. Sist. Inf., vol. 1, no. 1, pp. 1–7, 2020, doi: 10.30630/jitsi.1.1.2.

D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,” Comput. Eng. Sci. Syst. J., vol. 4, no. 1, p. 78, 2019, doi: 10.24114/cess.v4i1.11458.

A. Santoso, H., Putra, H. S., & Pratama, “Journal of technology information,” vol. 5, no. 2, pp. 99–104, 2020.

M. K. Efori Buulolo, S.Kom., Data Mining untuk Perguruan Tinggi. Medan: CV. Budi Utama, 2020.

C. R. Raharya, N. Hidayat, and E. Santoso, “Penentuan Penerimaan Beasiswa Menggunakan Metode Modified K-Nearest Neighbor | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4984–4990, 2018, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3158.

Abdul Muiz Khalimi, “Tahapan Dasar Algoritma kNN (k-Nearest Neighbor),” Pengalaman Edukasi, 2022. https://www.pengalaman-edukasi.com/2020/06/tahapan-dan-cara-kerja-yang-benar.html.

A. Suwarsih, “Identifikasi Potensi Nasabah Baru …,” J. Ekon. dan Bisnis Isllam, vol. 3, pp. 1–23, 2017.

Y. A. Setianto, K. Kusrini, and H. Henderi, “Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur,” Creat. Inf. Technol. J., vol. 5, no. 3, p. 232, 2019, doi: 10.24076/citec.2018v5i3.179.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Normalisasi Data Dengan Menggunakan Model Min Max Untuk Klasifikasi Nasabah Potensial Pada Bidang Pembelian Properti Menggunakan Algoritma K-Nearest Neighbor

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Viola Putri

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Jurnal INFORMASI DAN TEKNOLOGI ILMIAH (INTI)
P3M STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 
Email : ejurnal.stmikbudidarma@gmail.com

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License