Analisis Pengelompokan Penjualan Produk Silver Jewelry Pada CV. Borobudur Silver Menggunakan Metode Algoritma K-Means

Authors

  • Nurfahrani nurfahrani Universitas Ahmad Dahlan
  • Miftahurrahma Rosyda Universitas Ahmad Dahlan

DOI:

https://doi.org/10.12928/jstie.v12i2.28209

Keywords:

Algoritma K-Means, Clustering, Data Mining, Metode Elbow, Penjualan

Abstract

CV. Borobudur Silver adalah perusahaan di Yogyakarta yang bergerak di bergerak di bidang perdagangan perhiasan perak. Masalah yang masih terjadi pada toko adalah peningkatan permintaan suatu produk dari pelanggan tetapi toko tidak mampu memenuhi karena stok produk tidak tersedia. Hal tersebut terjadi karena kurang pemahaman terkait pola penjualan sehingga penyediaan stok produk tidak efisien. Oleh karena itu, dibutuhkan pengetahuan terkait pola penjualan dengan dilakukan pengelompokan data berdasarkan tingkat penjualan. Pengelompokan dapat dilakukan menggunakan metode clustering data mining dengan menerapkan algoritma k-means. Penelitian ini dimulai dengan tahap pembersihan data, penentuan nilai k dengan metode elbow, implementasi dengan algoritma k-means, dan pengujian dengan metode silhouette coefficient. Proses analisis menggunakan 526 data transaksi produk dari bulan Oktober sampai Desember tahun 2022. Pengelompokan Oktober, cluster tertinggi didominasi produk gelang kaki, cluster sedang didominasi produk anting, dan cluster rendah didominasi produk gelag tangan dengan hasil pengujian kualitas cluster sebesar 0,685. Pengelompokan November, untuk cluster tertinggi didominasi cincin, cluster sedang didominasi produk kalung, dan cluster rendah didominasi produk anting dengan hasil pengujian kualitas cluster sebesar 0,898. Pengelompokan Desember, cluster tertinggi didominasi produk bros plated, cluster sedang didominasi produk gelang tangan, dan cluster rendah didominasi produk anting dengan hasil pengujian kualitas cluster sebesar 0,823.

References

F. Redjeki, H. Fauzi, and S. Priadana, “Implementation of Appropriate Marketing and Sales Strategies in Improving Company Performance and Profits,” Int. J. Sci. Soc., vol. 3, no. 2, pp. 31–38, Apr. 2021, doi: 10.54783/ijsoc.v3i2.314.

D. J. Teece, “Business Models, Business Strategy and Innovation,” Long Range Plann., vol. 43, no. 2–3, pp. 172–194, Apr. 2010, doi: 10.1016/j.lrp.2009.07.003.

H. Siagian, Z. J. H. Tarigan, and F. Jie, “Supply Chain Integration Enables Resilience, Flexibility, and Innovation to Improve Business Performance in COVID-19 Era,” Sustainability, vol. 13, no. 9, p. 4669, Apr. 2021, doi: 10.3390/su13094669.

N. Normah, S. Nurajizah, and A. Salbinda, “Penerapan Data Mining Metode Kmeans Clustering untuk Analisa Penjualan pada Toko Fashion Hijab Banten,” J. Tek. Komput. AMIK BSI, vol. 7, no. 2, pp. 158–163, 2021, doi: https://doi.org/10.31294/jtk.v4i2.

M. H. Santoso, “Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom,” Brill. Res. Artif. Intell., vol. 1, no. 2, pp. 54–66, Dec. 2021, doi: 10.47709/brilliance.v1i2.1228.

M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means Algorithm: A Comprehensive Survey and Performance Evaluation,” Electronics, vol. 9, no. 8, p. 1295, Aug. 2020, doi: 10.3390/electronics9081295.

I. Safira, R. Salkiawati, and W. Priatna, “Penerapan Algoritma K-Means untuk Mengetahui Pola Persediaan Barang pada Toko Raja Bekasi,” J. Inform. Inf. Secur., vol. 3, no. 1, pp. 99–110, Jul. 2022, doi: 10.31599/jiforty.v3i1.1253.

A. P. Pramudiansyah, “Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary,” J. Ilm. Ilmu Komput., vol. 7, no. 2, pp. 06–19, 2021, doi: 10.35329/jiik.v7i2.201.

R. K. Dinata, S. Safwandi, N. Hasdyna, and N. Azizah, “Analisis K-Means Clustering pada Data Sepeda Motor,” INFORMAL Informatics J., vol. 5, no. 1, p. 10, 2020, doi: 10.19184/isj.v5i1.17071.

L. G. Rady Putra and A. Anggrawan, “Pengelompokan Penerima Bantuan Sosial Masyarakat dengan Metode K-Means,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 205–214, Nov. 2021, doi: 10.30812/matrik.v21i1.1554.

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Published

30-06-2024

Issue

Vol. 12 No. 2 (2024): Juni

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