Klasterisasi Pengunjung Mall untuk Menentukan Target Pasar Ponsel Terbaru Menggunakan Algoritma K-Means Clustering

Authors

  • Irvansah Satria Pamungkas Universitas Pamulang
  • Dhimas Maulana Hadi Universitas Pamulang
  • Gilang Aditya Universitas Pamulang
  • Rosyidah Astari Nur Universitas Pamulang
  • Aries Saifudin Universitas Pamulang http://orcid.org/0000-0002-3882-633X
  • Teti Desyani Universitas Pamulang

DOI:

https://doi.org/10.32493/informatika.v6i3.11629

Keywords:

K-means, Analysis, Cellphone, People, Clusters

Abstract

Every day there are always people who visit the mall with different needs, there are some people who visit the mall to buy cell phones that have the latest features to make them look more updated, some just look around and some are not recommend to buy a new one because he still thinks his phone can still be used. To overcome this, electronic goods sales shops, especially those selling cellphones, need an analysis when they want to sell the latest products. They can group those people with expenditure information and their stages using the Kmeans Clustering algorithm. The results of these people will be divided into several clusters, namely people who will buy, people who might buy or people who will not buy, so when these cellphone distributors want to sell the latest cellphones, they will know how many people have the opportunity to buy the cellphone.

References

Arofah, S. N., & Marisa, F. (2018, Mei). Penerapan Data Mining untuk Mengetahui Minat Siswa pada Pelajaran Matematika menggunakan Metode K-Means Clustering. JOINTECS (Journal of Information Technology and Computer Science), 3(2), 85-90. doi:10.31328/jointecs.v3i2.787

Fathia, A. N., Rahmawati, R., & Tarno. (2016). Analisis Klaster Kecamatan di Kabupaten Semarang Berdasarkan Potensi Desa Menggunakan Metode Ward dan Single Linkage. Jurnal Gaussian, 5(4), 801-810. doi:10.14710/j.gauss.v5i4.17109

Matplotlib development team. (2021, August 30). matplotlib.pyplot.bar. Retrieved from Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html

Nishom, M. (2019). Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square. JPIT (Jurnal Informatika: Jurnal Pengembangan IT), 4(1), 20-24. doi:10.30591/jpit.v4i1.1253

Retno, S. (2019, Juli 8). Peningkatan Akurasi Algoritma K-Means dengan Clustering Purity Sebagai Titik Pusat Cluster Awal (Centroid). Medan: Universitas Sumatera Utara.

Sardar, T. H., & Ansari, Z. (2018). An Analysis of MapReduce Efficiency in Document Clustering Using Parallel K-means. Future Computing and Informatics Journal, 3(2), 200-209. doi:10.1016/j.fcij.2018.03.003

scikit-learn developers (BSD License). (2021, August 30). sklearn.cluster.KMeans. Retrieved from scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html

Waskom, M. (2021, August 30). seaborn.scatterplot. Retrieved from Seaborn: https://seaborn.pydata.org/generated/seaborn.scatterplot.html

Downloads

Published

2021-09-30