Analisis K-Means dalam Segmentasi Pasar Penggunaan Handphone di Lingkungan Mahasiswa STMIK Royal
Keywords:
K-Means, Clustering, Data Mining, Market SegmentationAbstract
The use of smartphones in Indonesia has been steadily increasing each year. In the era of the Fourth Industrial Revolution, smartphones have become a lucrative business sector, leading to intense market competition. Consequently, smartphone companies must pay closer attention to the market segmentation desired by consumers. Data mining is the process of discovering significant relationships and patterns by analyzing large datasets using statistical and mathematical techniques. This study aims to identify and analyze the market segments of Android smartphone users among students at STMIK Royal. The data used in this research were collected from 122 student respondents. The study employs clustering using the K-means algorithm. The resulting data modeling will categorize market segments into several clusters. This segmentation yields three clusters: Cluster 1 (features), consisting of 36 respondents who prioritize price, battery, camera, and warranty; Cluster 2 (product), with 49 respondents who value all attributes except warranty; and Cluster 3 (superiority), comprising 37 respondents who prioritize camera, brand, and RAM.
References
F. Putri, A. Hasibuan, S. Sumarno, and I. Parlina, “Penerapan K-Means pada Pengelompokan Penjualan Produk Smartphone,” vol. 1, no. 1, pp. 15–20, 2021.
Z. A. Gultom, M. K. Kamil Erwansyah S.Kom, and M. Drs. Ahmad Calam S.Kom, “Implementasi Data Mining untuk Memprediksi Penghasilan Penjualan Handphone di Toko Ponsel Takasimura dengan Metode Rough Set,” Jurnal, pp. 1–14.
S. Early, “Penerapan K-Means pada Segmentasi Pasar untuk Riset Pemasaran pada,” vol. 9, no. 4, pp. 966–973, 2022, doi: 10.30865/jurikom.v9i4.4486.
M. K. Putri, J. S. F. M. Rahman, F. A. Nursyifa, S. Alfarisi, T. G. S. Putro, and R. Agustin, “Analisis Segmentasi Pasar Dalam Penggunaan Produk Viefresh Di Wilayah Sekitar Kampus Universitas Muhammadiyah Surabaya,” Balanc. Econ. Business, Manag. Account. J., vol. 16, no. 2, pp. 156–161, 2019, doi: 10.30651/blc.v16i2.3133.
E. P. Erik, “Analisis Segmentation Customer Mall Menggunakan Clustering Algoritma K-Means,” J. Ilmu Data, vol. 2, no. 11, pp. 1–11, 2022, [Online]. Available: http://ilmudata.org/index.php/ilmudata/article/view/266
G. Jotyka and I. G. Ketut Riski Suputra, “Prosedur Pendaftaran Dan Pengalihan Merek Serta Upaya Perlindungan Hukum Terhadap Merek Terkenal Menurut Undang-Undang Nomor 15 Tahun 2001,” Ganesha Law Rev., vol. 3, no. 2, pp. 125–139, 2021, doi: 10.23887/glr.v3i2.447.
M. F. Putra, A. H. Kridalaksana, and Z. Arifin, “Rancang Bangun Alat Pendeteksi Kebocoran Gas LPG Dengan Sensor Mq-6 Berbasis Mikrokontroler Melalui Smartphone Android Sebagai Media Informasi,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 12, no. 1, p. 1, 2017, doi: 10.30872/jim.v12i1.215.
S. Kusnaedi, M. Zubair, Z. S. P. Syariah, and undefined 2020, “Penerapan Akad Kafalah pada Jasa Garansi Bank BTN Syariah Parepare,” Ejurnal.Iainpare.Ac.Id, vol. 2, no. November, 2020, [Online]. Available: https://ejurnal.iainpare.ac.id/index.php/banco/article/view/1810
U. P. Utama, U. P. Utama, M. Fahzurrahman, and U. P. Utama, “PELATIHAN BEAUTY SHOT PHOTOGRAPHY DENGAN KAMERA HANDPHONE PADA MURID TATARIAS PENGANTIN,” vol. 2, no. 3, pp. 141–150, 2022.
A. Darmawan, N. Kustian, and W. Rahayu, “Implementasi Data Mining Menggunakan Model SVM untuk Prediksi Kepuasan Pengunjung Taman Tabebuya,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 2, no. 3, p. 299, 2018, doi: 10.30998/string.v2i3.2439.
D. Juliawan, F. Amir, and E. Desi, “Penerapan Data Mining Metode Clustering Pada CV. Secom Infotech Menggunakan Algoritma K-Means,” Pros. Semin. Nas. Ris. Inf. Sci., vol. 1, no. September, p. 96, 2019, doi: 10.30645/senaris.v1i0.12.
P. Pt and S. Tunggal, “Penerapan metode k-means clustering dalam pengelompokan data penjualan barang pada pt. swasti tunggal mandiri,” vol. 2, no. 3, pp. 205–210, 2022.
A. Aditya, I. Jovian, and B. N. Sari, “Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019,” J. Media Inform. Budidarma, vol. 4, no. 1, p. 51, 2020, doi: 10.30865/mib.v4i1.1784.
A. Prasatya, R. R. A. Siregar, and R. Arianto, “Penerapan Metode K-Means Dan C4.5 Untuk Prediksi Penderita Diabetes,” Petir, vol. 13, no. 1, pp. 86–100, 2020, doi: 10.33322/petir.v13i1.925.
Y. P. Sari, A. Primajaya, and A. S. Y. Irawan, “Implementasi Algoritma K-Means untuk Clustering Penyebaran Tuberkulosis di Kabupaten Karawang,” INOVTEK Polbeng - Seri Inform., vol. 5, no. 2, p. 229, 2020, doi: 10.35314/isi.v5i2.1457.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ade Febriyanti, Putri Vina Bancin, Siska Amanda
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
