Skripsi

IMPLEMENTASI ALGORITMA K-MEANS UNTUK PENGELOMPOKAN CUSTOMER CHURN DALAM MENENTUKAN STRATEGI PEMASARAN BANK

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Penilaian

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Customer churn, or the loss of customers, is a major challenge in the banking industry as it can lead to significant financial losses. This study aims to segment customers based on characteristics that influence churn risk using the K-Means algorithm. The data used is secondary data consisting of a bank customer churn dataset with 9,763 records obtained from the Kaggle platform, processed following the CRISP-DM framework. The clustering process was carried out using the RapidMiner application, with performance evaluation using the Davies Bouldin Index to determine the optimal number of clusters (K). The results indicate that the optimal value is achieved when K = 4. Centroid analysis shows that balance and estimated salary are the primary variables forming the clusters. Cluster 1 and Cluster 3 contain the highest number of churned customers. Cluster 1 consists of customers with high balances but low salaries, while Cluster 3 includes customers with both high balances and high salaries. This indicates that a high balance does not necessarily guarantee customer loyalty, and factors such as income play an important role in preventing customer churn. Based on these findings, the recommended strategies include offering low-risk investment products and encouraging customers to allocate idle funds into profitable investment instruments. This study demonstrates that the K-Means method is effective in generating relevant customer segmentation as a foundation for designing more targeted and efficient marketing strategies.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004427	T179610	T1796102025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1796102025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 117 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Sistem informasi

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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