Implementasi Algoritma K-Means untuk Clustering Nominal Pembayaran pada Odoo Versi 11 di PT.XYZ
Keywords:
ERP, Odoo, Data Mining, K-Means, ClusteringAbstract
Payment data in the Odoo version 11 system at PT. XYZ has not been optimally utilized to support strategic business decisions. This study implements the K-Means clustering algorithm to group customer payment amounts and identify transaction behavior patterns. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, while visualization through scatter plot and block plot was used to interpret the clustering results. The analysis produced three main clusters representing customers with high, medium, and low payment amounts. These segmentation results enable PT. XYZ to better understand customer payment behavior, optimize marketing strategies, enhance service quality, and improve cash flow management. Overall, this research demonstrates the effectiveness of the K-Means algorithm in processing payment data within the Odoo ERP system and highlights its potential to support more accurate and data-driven business decision-making.References
A. Aditya and H. F. Efendi, “Business Process Analysis and Implementation of Odoo Open Source ERP System in Inventory, Purchasing and Sales Activities,” Procedia Soc. Sci. Humanit., vol. 3, no. January, pp. 349-357, 2022, doi: 10.21070/pssh.v3i.180.
Maulana Ardiansyah and Supatman, “KLASTERISASI TAGIHAN PADA NASABAH PINJAMAN ONLINE MENGGUNAKAN METODE K-MEANS CLUSTERING,” J. RESTIKOM Ris. Tek. Inform. dan Komput., vol. 6, no. 2, pp. 286-294, 2024, doi: 10.52005/restikom.v6i2.318.
H. Mutaqin, “Penerapan Algoritma K-Means Pada Penjualan Saldo Transportasi Online Studi Kasus Konter XYZ,” J. Algoritm., vol. 20, no. 1, pp. 57-64, 2023, doi: 10.33364/algoritma/v.20-1.1244.
S. W. Harjono, N. W. Utami, and I. G. A. P. D. Putri, “Klasterisasi Tingkat Penjualan pada Startup Panak.id dengan Algoritma K-Means,” J. Ilm. Teknol. Inf. Asia, vol. 17, no. 1, pp. 55-66, 2023, doi: 10.32815/jitika.v17i1.888.
A. A. Alolayan and A. A. Alhamed, “Detection of Knowledge on Social Media Using Data Mining Techniques,” pp. 472-482, 2024, doi: 10.4236/ojapps.2024.142034.
T. Gori et al., “PREPROCESSING DATA DAN KLASIFIKASI UNTUK PREDIKSI KINERJA DATA PREPROCESSING AND CLASSIFICATION FOR PREDICTING STUDENT,” vol. 11, no. 1, pp. 215-224, 2024, doi: 10.25126/jtiik.20241118074.
R. Sistem, “Jurnal resti,” vol. 1, no. 10, pp. 117-122, 2021.
G. G. Dodi Alexsander Manalu, “IMPLEMENTASI METODE DATA MINING K-MEANS CLUSTERING TERHADAP DATA PEMBAYARAN TRANSAKSI MENGGUNAKAN BAHASA PEMROGRAMAN PYTHON PADA CV DIGITAL DIMENSI,” Raspberry Pi OS Syst. Adm. with Syst. Python, vol. 8, no. 1, pp. 175-305, 2023, doi: 10.1201/b23421-3.
M. R. Syahkur and D. Hartama, “Evaluasi Jumlah Cluster pada Algoritma K-Means ++ Menggunakan Silhouette dan Elbow dengan Validasi Nilai DBI dalam Mengelompokkan Gizi Balita,” vol. 13, no. 3, pp. 487-496, 2024.
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Copyright (c) 2025 Audrey Alicia Laseduw, Cahyono Budy Santoso
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