Implementasi K-Means Clustering Berbasis RapidMiner untuk Optimalisasi Segmentasi Penjualan Produk dalam Meningkatkan Efektivitas Strategi Pemasaran
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Sufajar Butsianto
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
- Arif Siswandi Universitas Pelita Bangsa, Bekasi, Indonesia (*) Corresponding Author
Abstract
The Indonesian electronic retail industry is experiencing rapid growth along with digital transformation. However, available sales data is often only stored as transaction records without further analysis, so it has not been optimally utilized for marketing decision making or customer segmentation. This study aims to implement the RapidMiner-based K-Means Clustering algorithm to analyze segmentation patterns of electronic products at XYZ Store. The dataset used includes the variables Transaction_ID, Product_ID, Product_Name, Category, Quantity, Unit_Price, Revenue, and Recency. The research stages include data collection, preprocessing (filtering, aggregation, and Z-Score normalization), K-Means application, and interpretation of clustering results. Determination of the number of clusters in this study uses the Elbow Method, which shows an optimal point at K = 3, so that number of clusters is chosen for the data grouping process. Based on the results of the application of the K-Means algorithm with the three clusters, the following are obtained: (1) Cluster 0 (High Sales & High Revenue) consisting of Smartphones, Laptops, and Tablets as superior products with a contribution of almost 60% of total revenue; (2) Cluster 1 (Medium Sales & Moderate Revenue) includes Televisions, Refrigerators, and Smartwatches with a stable contribution of around 27%; and (3) Cluster 2 (Low Sales & Low Revenue) contains Washing Machines, Speakers, Headphones, and Printers with a low contribution of only 14%. These findings provide a strategic basis for management in making business decisions, such as procurement priorities, seasonal promotions, product bundling, and clearance strategies. This study proves that the application of data mining with K-Means Clustering is effective in increasing operational efficiency and supporting the competitiveness of the electronics retail business in Indonesia.
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References
I. Shantilawati, S. Zebua, and R. Tarmizi, “Penggunaan Digital Marketing Dalam Meningkatkan Penjualan Bisnis Retail,” JMARI, vol. 5, no. 1, pp. 30–37, Feb. 2024, doi: 10.33050/jmari.v5i1.3130.
F. M. Alexander, A. Sasmitapura, M. Michael, and M. Meythi, “Faktor Finansial dan Nilai Perusahaan Ritel Indonesia,” Jurnal Akuntansi Kontemporer, vol. 15, no. 3, pp. 155–169, 2023, doi: 10.33508/jako.v15i3.4741.
N. Qomariah, “The Role of Promotion and Service Quality in Increasing Consumer Satisfaction and Loyalty in Pawnshops,” Journal of Economics, Finance And Management Studies, vol. 04, no. 10, Oct. 2021, doi: 10.47191/jefms/v4-i10-17.
M. I. Faidah and Z. Fatah, “Clustering K-Means Dengan Rapidminer Untuk Identifikasi Produk Terlaris,” JUSIFOR : Jurnal Sistem Informasi dan Informatika, vol. 4, no. 1, pp. 25–33, Jun. 2025, doi: 10.70609/jusifor.v4i1.5821.
S. N. Lathifah and Z. F. Azzahra, “AI-Driven Customers Segmentation Using K-Means Clustering,” G-Tech: Jurnal Teknologi Terapan, vol. 9, no. 1, pp. 320–329, Jan. 2025, doi: 10.70609/gtech.v9i1.6202.
M. Miranda and S. Sriani, “Implementation of K-Means Clustering in Grouping Sales Data at Zura Mart,” Journal of Applied Informatics and Computing, vol. 9, no. 2, pp. 547–555, Apr. 2025, doi: 10.30871/jaic.v9i2.9160.
L. Fernando and M. I. Fianty, “Optimizing Motorcycle Sales: Enhancing Customer Segmentation with K-Means Clustering and Data Mining Techniques,” Journal of Information Systems and Informatics, vol. 6, no. 3, pp. 1484–1498, Sep. 2024, doi: 10.51519/journalisi.v6i3.799.
B. I. Nugroho, A. Rafhina, P. S. Ananda, and G. Gunawan, “Customer segmentation in sales transaction data using k-means clustering algorithm,” Journal of Intelligent Decision Support System (IDSS), vol. 7, no. 2, pp. 130–136, Jun. 2024, doi: 10.35335/idss.v7i2.236.
A. Nugraha, Y. Amelia Effendi, N. Nicholas, Z. Tao, M. Afifuddin, and N. Nuzulita, “K-Means clustering interpretation using recency, frequency, and monetary factor for retail customers segmentation,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 23, no. 2, p. 435, Apr. 2025, doi: 10.12928/telkomnika.v23i2.26044.
M. I. Faidah and Z. Fatah, “Clustering K-Means Dengan Rapidminer Untuk Identifikasi Produk Terlaris,” JUSIFOR : Jurnal Sistem Informasi dan Informatika, vol. 4, no. 1, pp. 25–33, Jun. 2025, doi: 10.70609/jusifor.v4i1.5821.
M. Rafi Nahjan, Nono Heryana, and Apriade Voutama, “Implementasi Rapidminer dengan Metode Clustering K-Means untuk Analisa Penjualan Pada Toko OJ Cell,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 1, pp. 101–104, Jan. 2023, doi: 10.36040/jati.v7i1.6094.
Moehammad Nasri Abdoel Wahid, Sudarjo, Friyato, and R. H. Tirtosetianto, “Analisis Klaster Usaha Mikro Kecil di Jawa Timur Menggunakan Metode Agglomerative Clustering dengan Software Orange Data Mining,” Akademika, vol. 22, no. 1, pp. 15–21, Feb. 2024, doi: 10.51881/jak.v22i1.125.
A. Nur Rahmi and Yosaphat Ananda Mikola, “Implementasi Algoritma Apriori untuk Menentukan Pola Pembelian pada Customer (Studi Kasus : Toko Bakoel Sembako),” Information System Journal, vol. 4, no. 1, pp. 14–19, May 2021, doi: 10.24076/infosjournal.2021v4i1.561.
H. Mawarni, G. Testiana, and M. L. Dalafranka, “Implementasi Algoritma K-Means untuk Segmentasi Pelanggan Pada PT. Bintang Multi Sarana Cabang Tugumulyo,” Jurnal Komputer dan Informatika, vol. 11, no. 2, pp. 227–236, Oct. 2023, doi: 10.35508/jicon.v11i2.12478.
R. Siagian, P. Sirait, and A. Halim, “The Implementation of K-Means dan K-Medoids Algorithm for Customer Segmentation on E-commerce Data Transactions,” SISTEMASI, vol. 11, no. 2, p. 260, May 2022, doi: 10.32520/stmsi.v11i2.1337.
J. M. John, O. Shobayo, and B. Ogunleye, “An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market,” Analytics, vol. 2, no. 4, pp. 809–823, Oct. 2023, doi: 10.3390/analytics2040042.
M. I. Faidah and Z. Fatah, “Clustering K-Means Dengan Rapidminer Untuk Identifikasi Produk Terlaris,” JUSIFOR : Jurnal Sistem Informasi dan Informatika, vol. 4, no. 1, pp. 25–33, Jun. 2025, doi: 10.70609/jusifor.v4i1.5821.
N. Dwitiyanti, Siti Ayu Kumala, and Shinta Dwi Handayani, “Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 6, pp. 768–778, Dec. 2024, doi: 10.29207/resti.v8i6.5514.
A. Yahya and R. Kurniawan, “Implementasi Algoritma K-Means untuk Pengelompokan Data Penjualan Berdasarkan Pola Penjualan,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 1, pp. 350–358, Jan. 2025, doi: 10.57152/malcom.v5i1.1773.
Mifta Almaripat, Ahmad Faqih, and Ade Rizki Rinaldy, “Sales Data Classterization Analysis Using K-Means Method for Marketing Strategy Development,” Journal of Artificial Intelligence and Engineering Applications (JAIEA), vol. 4, no. 2, pp. 972–976, Feb. 2025, doi: 10.59934/jaiea.v4i2.792.
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