Application of Data Mining to Predict Distro Clothing Sales Using the K-Means Clustering Method
Penerapan Data Mining Untuk Prediksi Penjualan Baju Distro Menggunakan Metode K-Means Clustering
DOI:
https://doi.org/10.21070/ups.8243Keywords:
Data Mining, K-Means Clustering, Sales Prediction, customer segmentationAbstract
The rapid advancement of technology in the modern era has brought major changes to various aspects of life, including the business sector. This study aims to analyze data to group products based on their sales levels. The results are expected to help Aldi Store develop more effective marketing and inventory strategies to boost sales and competitiveness. Data processing was carried out using Google Colaboratory with the K-Means algorithm, combined with evaluation methods such as the Silhouette Coefficient and Davies-Bouldin Index. The findings show that K-Means is effective in clustering sales patterns of distro products. Evaluation results show a Silhouette Coefficient of 0.576, a Calinski-Harabasz Index of 19.125, and a Davies-Bouldin Index of 0.308, indicating high-quality clusters with strong cohesion and clear separation. This enables more accurate customer segmentation.
Downloads
References
R. G. Solechati and A. Jananto, “Penerapan Algoritma K-Means Clustering Pada Data Brain Stroke Untuk Pengelompokan Profile Pasien,” semanTIK, vol. 9, no. 1, p. 39, 2023, doi: 10.55679/semantik.v9i1.29446.
R. Maulana, “OPTIMISASI PENGGUNAAN ALGORITMA MACHINE LEARNING,” vol. 1, no. 6, pp. 1–16, 2024.
C. Purnama, W. Witanti, and P. Nurul Sabrina, “Klasterisasi Penjualan Pakaian untuk Meningkatkan Strategi Penjualan Barang Menggunakan K-Means,” J. Inf. Technol., vol. 4, no. 1, pp. 35–38, 2022, doi: 10.47292/joint.v4i1.79.
A. T. Suseno, A. R. Naufal, and M. Al Amin, “Market Based Analysis Sebagai Peningkatan Penjualan Produk Menggunakan Algoritma K-Medoids Dan Fp-Growth,” J. Tek. Inf. dan Komput., vol. 5, no. 2, p. 301, 2022, doi: 10.37600/tekinkom.v5i2.646.
P. Putra, “PENGEMBANGAN MODEL PREDIKSI RISIKO KREDIT,” vol. 1, no. 6, pp. 1–18, 2024.
O. B. Ginting, A. Anita, and E. Y. Tumanggor, “Penerapan Metode Trend Moment Untuk Memprediksi Jumlah Penjualan Dan Stok Kopi Pada Omilen Coffee,” J. Tekinkom (Teknik Inf. dan Komputer), vol. 7, no. 1, pp. 395–401, 2024, doi: 10.37600/tekinkom.v7i1.1332.
I. B. Perkasa and I. Komputer, “STRATEGI DATA MINING UNTUK IDENTIFIKASI POLA,” vol. 1, no. 6, pp. 1–16, 2024.
R. Dwi Putra, “Klasifikasi Penjualan Produk Customer Relationship Management dengan Algoritma K-Nearest Neighbors,” J. Comput. Scine Inf. Technol., vol. 8, pp. 48–55, 2022, doi: 10.35134/jcsitech.v8i2.34.
Ismai, “Metode Klasifikasi Menentukan Kenaikan Level UKM Bandung Timur Dengan Algoritma Naive Bayes Pada Sistem JURAGAN Berbasis Komunitas,” vol. 03, no. 01, pp. 24–31, 2020.
N. Farida, M. T. Chulkamdi, and Z. Wulansari, “Application of Data Mining By Using a Priori Algorithm To Improve Customer Purchasing Decisions At Mikamart Blitar Store,” Int. J. Multidiscip. Res. Lit., vol. 1, no. 5, pp. 526–534, 2022, doi: 10.53067/ijomral.v1i5.58.
R. Komansilan, V. Tarigan, and A. Yusupa, “Analisis Perbandingan Metode Trend Moment dan Regresi Linear Untuk Meramal Harga Saham Bank BRI,” J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 7, no. 1, p. 24, 2024, doi: 10.53513/jsk.v7i1.9474.
L. P. Dalova, Nurmawanti, N. E. Faizah, and S. B. Syahputro, “Efektifitas Penerapan Customer Relationship Management Pada Usaha Jasa Desain Iklan Citra Karya Setia (Advertising & Digital Printing) Melalui Pemasaran Electronic Word of Mouth (E-Wom),” Neraca Manajemen, Akunt. Ekon., vol. 1, no. 3, pp. 1–17, 2023.
A. Nugraha, O. Nurdiawan, and G. Dwilestari, “PENERAPAN DATA MINING METODE K-MEANS CLUSTERING UNTUK ANALISA PENJUALAN PADA TOKO YANA SPORT,” 2022.
N. Luh, P. P. Dewi, I. Nyoman Purnama, and N. W. Utami, “Penerapan Data Mining Untuk Clustering Penilaian Kinerja Dosen Menggunakan Algoritma K-Means (Studi Kasus: STMIK Primakara),” Jurnal Ilmiah Teknologi Informasi Asia, vol. 16, no. 2, 2022.
S. Keputusan Dirjen Penguatan Riset dan Pengembangan Ristek Dikti, I. Setiawan Mangku Negara, I. Ahmad Ashari, P. Studi Teknologi Informasi, F. Sains dan Teknologi, and U. Harapan Bangsa, “Terakreditasi SINTA Peringkat 4 Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means,” 2021.
Downloads
Additional Files
Posted
License
Copyright (c) 2025 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
