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Comparison of Decision Tree and K-Means Clustering Algorithms to Determine Awards for Customer Loyalty

Year: 2025

Type: article

Abstract: PT Tangguh Buana Roda Indonesia has difficulty in retaining loyal customers due to less than optimal customer management. This research proposes the use of a data mining-based system to categorize loyal customers using the K-Means and Decision Tree methods. The evaluation shows that the combination of K-Means and Decision Tree algorithms provides a higher average accuracy of 93.7175%. Compared to using Decision Tree alone which reached 92.8525% and K-Means which was only 91.667%. With the combination of these two algorithms, it is expected to support the awarding of loyal customers and strengthen the relationship between customers and companies. The system that has been created is web-based which will facilitate strategic planning to increase customer loyalty.

Source: Journal Technology Information and Data Analytic

DOI: 10.70491/tifda.v2i1.82

Authors: Bagus Tri Mahardika, Donnie Varyasetya Prastowo

Institution: Universitas Darma Persada

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411520703

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