ANALISIS SEGMENTASI PIUTANG MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING UNTUK OPTIMALISASI PENAGIHAN DI WANTI SALON

Alya Rachmadini Safitrie; Agus Bahtiar

Authors

Alya Rachmadini Safitrie STMIK IKMI Cirebon, Indonesia
Agus Bahtiar STMIK IKMI Cirebon, Indonesia

Keywords:

K-Means, Piutang Usaha, Segmentasi Pelanggan, data mining

Abstract

Effective accounts receivable management plays a critical role in maintaining cash flow stability and business continuity, especially in service sectors such as beauty salons. This study aims to apply the K-Means Clustering algorithm to segment customers based on receivables data from Wanti Salon. The dataset used consists of 1000 customer entries with primary attributes being billing amount and receivable age in days. The research follows the Knowledge Discovery in Databases (KDD) framework, encompassing data selection, preprocessing, transformation, data mining, and evaluation stages. Experiments were conducted using RapidMiner with cluster configurations ranging from K=2 to K=5. The results demonstrate that the configuration with K=3 yields the most optimal segmentation by categorizing customers into three risk groups: low, medium, and high. Performance evaluation using the within centroid sum of squares supports the finding that K=3 provides the best balance between cluster compactness and business interpretability. This segmentation enables management to design more effective and adaptive collection strategies. Therefore, the implementation of clustering algorithms such as K-Means proves to be a data-driven solution for receivables management and can be replicated in other service sectors to support more strategic and analytically grounded decision-making.

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