Profitability Analysis of Jordan Sneaker Resale Market Using Regression and K-Means Clustering to Support Sales Strategy
DOI:
https://doi.org/10.64803/jodsie.v1i2.34Keywords:
Data Mining, Random Forest, K-Means Clustering, Profit Prediction, Sneaker ResaleAbstract
The resale sneaker market has experienced significant growth and become an attractive business opportunity due to the difference between retail and resale prices. However, fluctuations in resale prices often make it difficult for sellers to determine the best selling time and sales channel to maximize profit. This study aims to analyze the profitability of the Jordan sneaker resale market using Random Forest Regression and K-Means Clustering. The dataset used consists of 5,000 sneaker transaction records containing retail price, resale price, profit margin, sales channel, sales date, and inventory duration. Random Forest Regression was applied to predict profit, while K-Means Clustering was used to segment products based on profitability characteristics. The regression model achieved a Mean Absolute Error (MAE) of 1.41 and an R² value of 1.00, indicating excellent predictive performance. Clustering results produced three product segments with different profitability levels. The findings show that resale price is the most influential factor affecting profit, Walk-in Retail generates the highest average profit, and May is the most profitable month for selling sneakers. These results can support data-driven decision making in resale sneaker businesses.
References
[1]. Kapti, K., & Astuti, D. (2025). Implementation of the K-Means Clustering Algorithm for Customer Segmentation. Sistemasi, 14(6), 2751–2767. https://doi.org/10.32520/stmsi.v14i6.5573
[2]. Nugroho, B. I., Rafhina, A., Ananda, P. S., & Gunawan, G. (2024). Customer Segmentation in Sales Transaction Data Using K-Means Clustering Algorithm. Journal of Intelligent Decision Support System, 7(2). https://doi.org/10.35335/idss.v7i2.236
[3]. Mawarni, H., Testiana, G., & Dalafranka, M. L. (2023). Implementasi Algoritma K-Means Untuk Segmentasi Pelanggan Pada PT. Bintang Multi Sarana Cabang Tugumulyo. J-ICON, 11(2). https://doi.org/10.35508/jicon.v11i2.12478
[4]. Djun, S. F., Gunadi, I. G. A., & Sariyasa, S. (2024). Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM. JTIM, 5(4), 354–364. https://doi.org/10.35746/jtim.v5i4.434
[5]. Kristanti, B. T., Junaidi, A., & Mandyartha, E. P. (2024). Implementasi K-Means Clustering dalam Segmentasi Pelanggan Berdasarkan Usia, Pendapatan, dan Model RFM. Jurnal Informatika dan Teknik Elektro Terapan, 12(3). https://doi.org/10.23960/jitet.v12i3.4677
[6]. Yulisasih, B. N., Herman, H., & Sunardi, S. (2024). K-Means Clustering Method for Customer Segmentation Based on Potential Purchases. ELTIKOM, 8(1). https://doi.org/10.31961/eltikom.v8i1.1137
[7]. Hidayat, R., Saputra, H. T., Husnah, M., Nabila, N., Hidayatullah, M. B., Nazhmi, M. N., Azra, J., & Rana, A. (2025). Implementasi Algoritma Random Forest Regression Untuk Memprediksi Penjualan Produksi di Supermarket. SIMKOM, 10(1), 101–109. https://doi.org/10.51717/simkom.v10i1.703
[8]. Azzahra, L., & Yasir, A. (2024). Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food. Jurnal Ilmu Komputer dan Sistem Informasi, 3(1). https://doi.org/10.70340/jirsi.v3i1.88
[9]. Putri, Y., Aldo, D., & Ilham, W. (2024). Retail Marketing Strategy Optimization: Customer Segmentation with Artificial Intelligence Integration and K-Means Clustering. Sinkron : Jurnal dan Penelitian Teknik Informatika, 8(4), 2155–2163. https://doi.org/10.33395/sinkron.v8i4.14000
[10]. Agustina, A., Tukino, T., Huda, B., & Novalia, E. (2025). Prediksi Volume Penjualan Gadget Berdasarkan Promo dan Channel Penjualan Menggunakan Random Forest. JUSIFOR, 4(1). https://doi.org/10.70609/jusifor.v4i1.6962
[11]. Yani, A., Azmi, Z., & Suherdi, D. (2023). Implementasi Data Mining Menganalisa Data Penjualan Menggunakan Algoritma K-Means Clustering. Jurnal Sistem Informasi Triguna Dharma, 2(2), 315–323. https://doi.org/10.53513/jursi.v2i2.6357
[12]. Widiyanto, A. T., & Witanti, A. (2021). Segmentasi Pelanggan Berdasarkan Analisis RFM Menggunakan Algoritma K-Means Sebagai Dasar Strategi Pemasaran. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 1(1), 204–215. https://doi.org/10.24002/konstelasi.v1i1.4293
[13]. Pramudiansyah, A. P. (2021). Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary. Jurnal Ilmiah Ilmu Komputer, 7(2), 6–19. https://doi.org/10.35329/jiik.v7i2.201
[14]. Kadarsah, D., & Heikal, J. (2024). Customer Segmentation With K-Means Clustering Suzuki Mobil Bandung Customer Case Study. Jurnal Indonesia Sosial Teknologi, 5(3), 768–774. https://doi.org/10.59141/jist.v5i3.935
[15]. Mulyadi, I. P. (2022). Clusterisasi Menggunakan Metode Algoritma K-Means dalam Meningkatkan Penjualan Tupperware. Jurnal Informatika Ekonomi Bisnis, 4(4), 323–328. https://doi.org/10.37034/infeb.v4i4.164
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Copyright (c) 2026 Ahmad Al fajri, Rega Esa Ramadhan, Wahyu Omar Gibran (Author)
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