CLUSTERING DATA PENJUALAN TOKO HELM KARTINI MENGGUNAKAN METODE K-MEANS
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Abstract
This study aims to design a K-Means clustering-based system that can help Kartini Helmet Store in grouping helmet sales data based on product category. With the increasing need for deeper data analysis to support business strategies, this store faces challenges in understanding sales patterns and customer preferences. This system utilizes the K-Means clustering algorithm implemented through the Python programming language to group sales data, facilitate trend analysis, and provide visualization of clustering results. The data used are store sales records in Excel format, which are then processed through stages starting from preprocessing to evaluating clustering results using the silhouette coefficient method to ensure clustering accuracy. The results of this study are expected to help stores in making decisions related to stock management, adjusting marketing strategies, and increasing customer satisfaction.
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References
[1]
A. V. Gheorghe, C. Lucaci, and M. G. Gheorghe, "Motorcycle helmets: A review of the effectiveness and standards," Safety Science, vol. 118, pp. 93-99, Jan. 2019.
[2]
M. N. Setiawan, S. A. Rachman, and T. Prasetyo, "Analysis of Factors Affecting Motorcycle Helmet Sales in Retail Stores," Journal of Retail and Consumer Services, vol. 53, pp. 200-210, Sept. 2020.
[3]
P. Alkhairi and A. P. Windarto, “Penerapan K-Means Cluster Pada Daerah Potensi Pertanian Karet Produktif di Sumatera Utara,” Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), vol. 1, no. 1, pp. 762–767, 2019, [Online]. Available: https://seminar-id.com/semnas-sainteks2019.html
[4]
A. A. Rismayadi, N. N. Fatonah, and E. Junianto, “Algoritma K-Means Clustering Untuk Menentukan Strategi Pemasaran Di Cv. Integreet Konstruksi,” J. Responsif Ris. Sains dan Inform., vol. 3, no. 1, pp. 30–36, 2021.
[5]
K. Arai and A. R. Barakbah, “Hierarchical K-means: an algorithm for centroids initialization for K-means,” Reports of the Faculty of Science and Engineering, vol. 36, no. 1, pp. 25–31, 2007.
[6]
S. Syarif, “Pengenalan Dasar Machine Learning dan Aplikasinya,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 1, pp. 50-56, 2022.
[7]
L. V Bijuraj, “Clustering and its Applications,” in Proceedings of National Conference on New Horizons in IT - NCNHIT, Coimbatore, 2013, pp. 169–172.
[8]
A. Kumar and S. V. Bhatia, "A survey on clustering algorithms and K-Means," Journal of Computing and Information Technology, vol. 27, no. 4, pp. 229-244, 2019.
[9]
A. Saputra, B. Mulyawan, and T. Sutrisno, “REKOMENDASI LOKASI WISATA KULINER DI JAKARTA MENGGUNAKAN METODE K-MEANS CLUSTERING DAN SIMPLE ADDITIVE WEIGHTING,” Jurnal Ilmu Komputer dan Sistem Informasi, vol. 7, no. 1, pp. 14–21, 2019, doi: https://doi.org/10.24912/jiksi.v7i1.5773.
[10]
Y. T. Wang, J. Liu, dan S. Zhang, "Evaluating Clustering Algorithms with Silhouette Score," Journal of Data Science and Analytics, vol. 8, no. 2, pp. 95-104, 2022.
[11]
D. S. Utami, D. Retno, and S. Saputro, “PENGELOMPOKAN DATA YANG MEMUAT PENCILANDENGAN KRITERIA ELBOWDAN KOEFISIEN SILHOUETTE (ALGORITME KMEDOIDS),” in Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya, 2018.
[12]
P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J Comput Appl Math, vol. 20, pp. 53–65, 1987.