Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services

Ana Fatahali Ramadhan; Sudin Saepudin; Carti Irawan; Mupaat

doi:10.56705/ijodas.v6i3.316

Authors

Ana Fatahali Ramadhan Universitas Nusa Putra
Sudin Saepudin Universitas Nusa Putra
Carti Irawan Universitas Nusa Putra
Mupaat Universitas Nusa Putra

DOI:

https://doi.org/10.56705/ijodas.v6i3.316

Keywords:

K-Means Clustering, Data Mining, Digital Printing, Python, Best-selling Services

Abstract

The digital printing industry in Indonesia is experiencing rapid growth thanks to the increasing demand from companies for printing services such as banners, stickers, brochures, and business cards. CV. Copy Paste is one of the companies operating in the digital printing industry that fulfills various printing orders every month. However, the company has difficulty identifying the most popular printing services, which makes it difficult to develop a targeted promotional strategy. In view of this problem, the aim of this study is to group digital printing services according to their popularity using the K-Means Clustering method. This study uses a quantitative approach, collecting sales data from the last 12 months, covering 160 types of services. The steps taken include preliminary data processing, namely attribute selection, data cleaning, and data transformation so that it can be effectively processed using the K-Means algorithm, implemented in the Python programming language. The test results show that digital printing services can be divided into three clusters: 115 less popular services (C1), 31 fairly popular services (C2), and 14 very popular services (C3). The results of this study provide information that can be used as a basis for strategic decisions regarding promotion and service management. In this way, the K-Means Clustering algorithm has proven effective in helping companies group products in a more objective and measurable way based on historical data.

Downloads

Download data is not yet available.

References

[1] K. Saharja and R. Gobal, “Pengaruh Waktu Proses Produksi Digital Printing Terhadap Kepuasan Konsumen Pengguna Produk Cetak,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 1, pp. 458–469, 2021, doi: 10.30645/j-sakti.v5i1.339.

[2] Bhinneka, “Masa Depan Bisnis Digital Printing di Indonesia,” Bhinneka Update, May 14, 2024.

[3] Printing Global Market Report 2025. 2025.

[4] M. F. Haryanti et al., “Pengaruh Data Mining, Strategi Perusahaan Terhadap Laporan Kinerja Perusahaan,” J. Manaj. dan Bisnis, vol. 3, no. 1, pp. 71–90, 2024, doi: 10.70704/jpjmb.v3i1.285.

[5] H. S. Nugraha, H. Mutaqin, A. Fathah, and C. Juliane, “Mengidentifikasi Strategi Promosi pada Jasa Penjualan Saldo Digital menggunakan Pendekatan Clustering,” Edumatic J. Pendidik. Inform., vol. 7, no. 1, pp. 11–19, 2023, doi: 10.29408/edumatic.v7i1.7385.

[6] Z. I. Alfianti, M. A. Azis, and A. Fauzi, “Grouping of Covid-19 Affected Areas in Bogor City Using The K-Means Algorithm,” J. Mantik, vol. 4, no. 4, pp. 2336–2341, 2021, doi: https://doi.org/10.35335/mantik.Vol4.2021.1142.pp2336-2341.

[7] V. S. Moertini, “Data Mining Sebagai Solusi Bisnis,” Integral, vol. 7, no. 1, 2002.

[8] A. F. AlShammari, “Implementation of Clustering using K-Means in Python,” Int. J. Comput. Appl., vol. 186, no. 40, pp. 12–17, Sep. 2024, doi: 10.5120/ijca2024923990.

[9] G. Gunadi, “Penerapan Algoritma K-Means Clustering Untuk Menganalisa Transaksi Penjualan Jasa Cetak Pada Unit Print on Demand (Pod) Percetakan Gramedia,” Infotech J. Technol. Inf., vol. 8, no. 2, pp. 117–126, 2022, doi: 10.37365/jti.v8i2.148.

[10] Wanto, Anjar, M. N. H. Siregar, and and A. P. Windarto, Data Mining : Algoritma Dan Implementasi, 1st ed. Yayasan Kita Menulis., 2020.

[11] I. Taufik, N. Sa’adah, N. Suparna, C. Alam, and P. Dauni, “Scoring System and K-Means Algorithm for Mutaba’ah Yaumiyah Activity,” 2020, doi: 10.4108/eai.11-7-2019.2297566.

[12] N. K. Zuhal, “Study Comparison K-Means Clustering dengan Algoritma Hierarchical Clustering,” Pros. Semin. Nas. Teknol. Dan Sains, vol. 1, pp. 200–205, 2022, doi: https://doi.org/10.29407/stains.v1i1.1495.

[13] A. Yani, Z. Azmi, and D. Suherdi, “Implementasi Data Mining Menganalisa Data Penjualan Menggunakan Algoritma K-Means Clustering,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 2, no. 2, p. 315, 2023, doi: 10.53513/jursi.v2i2.6357.

[14] M. Rochmawati, G. W. C. Bagaskara, I. A. Adha, Y. Umaidah, and A. Voutama, “Implementation of the K-Means Algorithm in Sales Clustering at a Company using the KDD Methodology,” SISTEMASI, vol. 13, no. 1, p. 54, Jan. 2024, doi: 10.32520/stmsi.v13i1.3074.

[15] D. Wu and L. Xin, “HC-means clustering algorithm for precision marketing on e-commerce platforms,” Syst. Soft Comput., vol. 7, 2025, doi: https://doi.org/10.1016/j.sasc.2025.200236.

[16] P. Kirst, T. Bajbar, and M. Merkel, “A bisection method for solving distance-based clustering problems globally,” TOP, vol. 33, no. 3, pp. 437–469, 2025, doi: 10.1007/s11750-024-00684-w.

[17] R. S. Chauhan, A. Munshi, and A. Pradhan, “The Role of Python in Enhancing Radiotherapy Department Workflow Efficiency and Promoting Open-source Software Utilisation,” Clin. Oncol., vol. 45, p. 103897, Sep. 2025, doi: 10.1016/J.CLON.2025.103897.

[18] L. Simorangkir, E. Sany, and M. F. N, “Penerapan Metode K-Means Untuk Pengelompokan Data Kunjungan Wisata Pada Dinas Kebudayaan Dan Pariwisata Provinsi Jambi,” J. Akad., vol. 17, no. 2, pp. 35–40, Jun. 2025, doi: 10.53564/akademika.v17i2.1496.

[19] K. Rakesh et al., Python for Beginners : A Comprehensive Guide to Learning Python Programming, First Edit., no. October. Warta Saya, 2024.

[20] M. Suyal and S. Sharma, “A Review on Analysis of K-Means Clustering Machine Learning Algorithm based on Unsupervised Learning,” J. Artif. Intell. Syst., vol. 6, no. 1, pp. 85–95, 2024, doi: 10.33969/ais.2024060106.

[21] M. Syukron Nawawi, F. Sembiring, and A. Erfina, “Implementasi Algoritma K-Means Clustering Menggunakan Orange Untuk Penentuan Produk Busana Muslim Terlaris,” Progr. Stud. Tek. Inform. Pgri Madiun, pp. 789–797, 2021.

[22] E. Omol, D. Onyangor, L. Mburu, and P. Abuonji, “Application Of K-Means Clustering For Customer Segmentation In Grocery Stores In Kenya,” Int. J. Sci. Technol. Manag., vol. 5, no. 1, pp. 192–200, 2024, doi: 10.46729/ijstm.v5i1.1024.

[23] A. J. Alifah, S. Saepudin, and C. Irawan, “Implementation of the K-Means Clustering Algorithm in Analyzing Public Satisfaction Regarding Public Services ( Studi Case : Balai Pengujian Standar Instrumen Tanaman Industri Dan Penyegar ) Implementasi Algoritma K-Means Clustering Dalam Menganalisis Kep,” vol. 5, no. 4, pp. 487–496, 2024, doi: 10.52436/1.jutif.2024.5.4.2125.

[24] F. Zafira, B. Irawan, and A. Bahtiar, “Penerapan Data Mining Untuk Estimasi Stok Barang Dengan Metode K-Means Clustering,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 156–161, 2024, doi: 10.36040/jati.v8i1.8319.

[25] GeeksforGeeks, “KDD Process in Databases.” Accessed: Jun. 06, 2025.

[26] C. W. Id, “The impact of neglecting feature scaling in k-means clustering,” pp. 1–19, 2024, doi: 10.1371/journal.pone.0310839.

[27] A. A. Khan, M. S. Bashir, A. Batool, M. S. Raza, and M. A. Bashir, “K-Means Centroids Initialization Based on Differentiation Between Instances Attributes,” Int. J. Intell. Syst., vol. 2024, no. 1, 2024, doi: 10.1155/2024/7086878.

[28] R. Afifa, M. I. Mazdadi, T. H. Saragih, F. Indriani, and M. Muliadi, “Implementasi Principal Component Analysis (PCA) dan Gap Statistic untuk Clustering Kanker Payudara pada Algoritma K-Means,” SISTEMASI, vol. 13, no. 5, p. 1852, Sep. 2024, doi: 10.32520/stmsi.v13i5.4015.

[29] J. Meng et al., “Heliyon Nano-integrating green and low-carbon concepts into ideological and political education in higher education institutions through K-means clustering,” Heliyon, vol. 10, no. 10, p. e31244, 2024, doi: 10.1016/j.heliyon.2024.e31244.

[30] T. P. Scholdra, J. R. K. Wichmann, and W. J. Reinartz, “Reimagining personalization in the physical store,” J. Retail., vol. 99, no. 4, pp. 563–579, 2024, doi: 10.1016/j.jretai.2023.11.001.