Penerapan Algoritma K-Means Clustering dalam Analisis Pengelompokan Produk Toko Oleh-Oleh Berdasarkan Data Penjualan
-
Chairunnita Chairunnita
*
Universitas PGRI Semarang, Semarang,
Indonesia
- Agung Handayanto Universitas PGRI Semarang, Semarang, Indonesia
- Febrian Murti Dewanto Universitas PGRI Semarang, Semarang, Indonesia (*) Corresponding Author
Abstract
Toko Oleh-Oleh Dury Weleri faces challenges in inventory management and promotional strategy due to the limitations of a conventional sales data recording system. This study aims to classify products based on sales performance using the K-Means Clustering algorithm by analyzing total sales, average sales, and remaining stock attributes. The optimal number of clusters was determined through a combination of the Elbow Method, Silhouette Score, and Davies-Bouldin Index, resulting in four main clusters. Cluster 0 consists of products with low sales and high stock (indicating potential overstock), Cluster 1 includes products with high sales but low stock (key products), Cluster 2 comprises products with moderate sales and relatively high stock (requiring light promotions), and Cluster 3 contains products with low sales and very low stock (likely seasonal or low-priority items). The clustering evaluation produced a Silhouette Score of 0.47336 and a DBI of 0.72644, indicating a reasonably good grouping quality. Interactive visualization via Streamlit provided strategic insights for decision-making regarding restocking and promotional planning. These findings are expected to support management in optimizing inventory control, improving operational efficiency, and developing more targeted sales strategies.
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References
R. I. Manarung, E. Widodo, and A. M. Rifai, “Sales Data Clustering Using the K-Means Algorithm to Determine Retail Product Needs,” Int. J. Softw. Eng. Comput. Sci., vol. 5, no. 1, pp. 226–234, 2025, doi: 10.35870/ijsecs.v5i1.4090.
O. N. Nugraha and G. Dwilestari, “Penerapan Data Mining Metode K-Means Clustering Untuk Analisa Penjualan Pada Toko Yana Sport,” JATI (Jurnal Mhs. Tek. Inform.), vol. 6, no. 2, pp. 849–855, 2022, doi: 10.36040/jati.v6i2.5755.
H. Al Rasyid, B. F. K. Soebari, and D. S. Y. Kartika, “Implementasi algoritma K-Means clustering untuk pengelompokan penjualan produk pada online shop Toko Gizi,” in Prosiding Seminar Nasional Teknologi dan Sistem Informasi, 2022, pp. 242–248.
S. H. Widiastuti and R. Jumardi, “Pengelompokan Daerah Rawan Demam Berdarah dengan Metode K-Means Clustering,” J. Inf. dan Teknol., pp. 185–190, 2022.
L. Adhitama, S. Murniati, and C. S. Pramudyo, “Minimasi Jarak Pengiriman Roti CV. Twin Setia dengan Metode K-Means Clustering dan Simulated Annealing,” J. Tek. SILITEK, vol. 3, no. 2, pp. 68–75, 2023.
A. N. B. Prasetyo, M. Maimunah, and P. Sukmasetya, “K-Means Clustering Method for Determining Waste Transportation Routes to Landfill,” J. Ris. Inform., vol. 5, no. 3, pp. 277–284, 2023.
J. Jabbar, “Sistem Informasi Stok Barang Menggunakan Metode Clustering Kmeans (Studi Kasus Rmd Store),” INFOTECH J., vol. 8, no. 1, pp. 70–75, 2022.
K. Hidayat, M. R. Adytama, H. A. Darmawan, Y. Arnando, and A. Mukarim, “Analisis Data Penjualan Menggunakan Algoritma K-Means Clustering pada Toko Superindo,” JoDMApps (Journal Data Sci. Methods Appl., vol. 1, no. 1, pp. 1–6, 2025, [Online]. Available: https://journal.darmajaya.ac.id/index.php/JoDMApps/article/view/904
W. T. Pambudi and A. Witanti, “Penerapan Algoritma K-Means Untuk Menganalisis Data Penjualan Pada Toko Ayu Collection Berasis Web,” J. Sist. Inf. dan Bisnis Cerdas, vol. 15, no. 1, pp. 1–9, 2022.
S. Lestari, M. Muhatri, A. R. Fachrezi, M. A. Sutrisno, and M. Geovany, “Implementasi Algoritma K-Means Clustering pada Penjualan Sepatu Futsal Merk Specs,” Polyg. J. Ilmu Komput. dan Ilmu Pengetah. Alam, vol. 2, no. 3, pp. 50–61, 2024.
K. Tabianan, S. Velu, and V. Ravi, “K-means clustering approach for intelligent customer segmentation using customer purchase behavior data,” Sustainability, vol. 14, no. 12, p. 7243, 2022.
A. Rifqi and R. T. Aldisa, “Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 486–497, 2023, doi: 10.47065/bits.v5i2.4278.
N. Ameliana, N. Suarna, and W. Prihartono, “Analisis Data Mining Pengelompokkan Umkm Menggunakan Algoritma K-Means Clustering Di Provinsi Jawa Barat,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 3261–3268, 2024, doi: 10.36040/jati.v8i3.9655.
R. S. Lisudatu, M. Marchelin, and L. K. Wibisono, “Klasifikasi UMKM Berdasarkan Kinerja Keuangan Menggunakan Algoritma K-Means Clustering: Studi Kasus UMKM Sektor Industri di Kecamatan Rantepao,” J. Bisnis Mhs., vol. 5, no. 2, pp. 891–900, 2025.
M. Miranda and S. Sriani, “Implementation of K-Means Clustering in Grouping Sales Data at Zura Mart,” JAIC, vol. 9, no. 2, pp. 547–555, 2025.
F. Fauji and L. Farokhah, “Penerapan Data Mining Menggunakan K-Means Clustering Dalam Mengelompokkan Tingkat Kesulitan Mata Pelajaran,” J. Inf. Syst. Res., vol. 6, no. 3, pp. 1705–1714, 2025, doi: 10.47065/josh.v6i3.6959.
F. Pangestu, N. Yasin, R. C. Hasugian, and Yunita, “Penerapan Algoritma K-Means Untuk Mengklasifikasi Data Obat,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 1, pp. 53–62, 2023, doi: 10.32736/sisfokom.v12i1.1461.
F. Yunita, “Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru (Studi Kasus: Universitas Islam Indragiri),” J. Sist., vol. 7, no. 3, pp. 238–249, 2018.
R. R. Muhima, M. Kurniawan, S. Kom, M. Kom, and A. Yudhana, Kupas tuntas algoritma clustering: Konsep, perhitungan manual, dan program. Penerbit Andi, 2022.
I. F. Ashari, E. D. Nugroho, R. Baraku, I. N. Yanda, and R. Liwardana, “Analysis of elbow, silhouette, Davies-Bouldin, Calinski-Harabasz, and rand-index evaluation on k-means algorithm for classifying flood-affected areas in Jakarta,” J. Appl. Informatics Comput., vol. 7, no. 1, pp. 95–103, 2023.
M. R. Syahkur, D. Hartama, and S. Solikhun, “Evaluasi Jumlah Cluster pada Algoritma K-Means++ Menggunakan Silhouette dan Elbow dengan Validasi Nilai DBI dalam Mengelompokkan Gizi Balita,” JST (Jurnal Sains dan Teknol., vol. 13, no. 3, 2024.
H. Mulyani, R. A. Setiawan, and H. Fathi, “Optimization of K value in clustering using silhouette score (case study: Mall customers data),” J. Inf. Technol. Its Util., vol. 6, no. 2, pp. 45–50, 2023.
I. T. Utami, F. Suryaningrum, and D. Ispriyanti, “K-Means Cluster Count Optimization With Silhouette Index Validation And Davies Bouldin Index (Case Study: Coverage Of Pregnant Women, Childbirth, And Postpartum Health Services In Indonesia In 2020),” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 707–716, 2023.
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