Skripsi

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

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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.

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Availability

Detail Information

Series Title

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Call Number

T1571312024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 58 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.312 07

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Data mining
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

TUTI

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