Citation :

Frisca Adiesthy Irdiani, Tuga Mauritsius, "Customer Segmentation using Data Mining Technique Method Case: Sarinah Department Store," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 8, pp. 154-164, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I8P110

Abstract

In this digitized era, implementing customer transaction data is essential, particularly in the case of department stores such as Sarinah, which have a wide range of customers whose purchasing habits vary widely. Sarinah records large transaction data, but most is used only to present sales reports and is rarely explored in-depth to understand customer behavior, customer value, or customer loyalty. As a result, marketing at Sarinah continues to be rather wide and undiversified and has a limited impact on day-to-day business decisions. This study aims to apply advanced data analysis techniques to segment the customers of Sarinah based on their transactions from 1st March 2022 to 9th September 2025. Data cleaning and normalization are performed before this procedure. Then, feature extraction is done with Recency, Frequency, and Monetary (RFM). After that, the K-Means, K-Medoids, Fuzzy C-Means, and DBSCAN clustering algorithms are used. Then the resulting clusters are evaluated by using the Davies-Bouldin Index (DBI). The results suggest that the best cluster is formed by the 4 clusters in K-Means with a DBI score of 0,571703631365945. There are four distinct types of customers: new, occasional, inactive/at-risk, and loyal. These segments have differing characteristics with respect to their shopping behavior, values, and possibilities for loyalty. This study provides a customer segmentation methodology that can be applied generally to the retailing sector to obtain focused marketing, customer retention, and better management strategies for the Sarinah Department Store.

Keywords

Customer segmentation, Clustering algorithms, Data mining, Sarinah club, Sarinah department store.

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