SEGMENTASI PELANGGAN MENGGUNAKAN ALGORITMA K-MEANS DENGAN ELBOW METHOD PADA SHOP CUSTOMER DATA

SIMANGUNSONG, TAVETO GUNTAR PARTOGI and Primartha, Rifkie (2024) SEGMENTASI PELANGGAN MENGGUNAKAN ALGORITMA K-MEANS DENGAN ELBOW METHOD PADA SHOP CUSTOMER DATA. Undergraduate thesis, Sriwijaya University.

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Abstract

Customer segmentation is a significant application of data analysis in business. This research uses the K-Means algorithm to group customer data based on transaction habits, with parameters from the dataset as cluster determinants. To determine the optimal number of clusters, the Elbow Method is applied, which is based on the highest difference of inertia values. The results show that 2 clusters are the most optimal, with a difference in inertia value of 911,735, compared to 4 clusters without Elbow Method, which results in a difference in inertia value of 387,996. The difference is shown through a line chart graph that forms an 'elbow', indicating the optimal point of the number of clusters. This finding shows that the Elbow Method is effective in optimizing the number of clusters, thereby improving the accuracy and relevance of customer segmentation in business analysis. Thus, this method can help companies design more targeted marketing strategies based on the customer segments formed.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Algoritma K-Means, Analisis Bisnis, Elbow Method, Segmentasi
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Taveto G.P. Simangunsong
Date Deposited:	11 Sep 2024 07:37
Last Modified:	11 Sep 2024 07:37
URI:	http://repository.unsri.ac.id/id/eprint/157131

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