Abdillah, Azqia Adistya (2024) PERBANDINGAN AKURASI ALGORITMA K-MEANS CLUSTERING MURNI DENGAN GABUNGAN K-MEANS CLUSTERING DAN ELBOW METHOD MENGGUNAKAN SILHOUTTE SCORE UNTUK SEGMENTASI PENGUNJUNG MALL. Diploma thesis, UPN Veteran Yogyakarta.
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Abstract
The rapid growth of the market compels companies to adopt more sophisticated
marketing strategies to maintain their competitiveness in an increasingly tight market. The
significant daily increase in the number of consumers presents a challenge for businesses in
meeting each customer's needs with optimal efficiency. Consequently, customer
segmentation has become essential, enabling businesses to divide customers into groups
based on their common characteristics to target each group more effectively.
This study aims to compare the accuracy of customer segmentation processes at
malls by integrating the K-Means Clustering algorithm with the Elbow Method using the
Silhouette Score as a parameter. This method was chosen for its ability to produce more
accurate and well-defined clusters.
Through the experiments conducted, the comparison results show that using pure K�Means Clustering yields a Silhouette Score of 0.2527, whereas the combination of K-Means
Clustering with the Elbow Method achieves a Silhouette Score of 0.3917. These results
significantly influence the outcome of the clustering.
With these conclusions, this study can serve as a reference for selecting the method
with the least potential for grouping errors, there by minimizing the risk of errors in
marketing strategies.
Keywords: Customer Segmentation, K-Means Clustering, Elbow Method, Silhouette Score
| Item Type: | Thesis (Diploma) |
|---|---|
| Uncontrolled Keywords: | Customer Segmentation, K-Means Clustering, Elbow Method, Silhouette Score |
| Subjects: | H Social Sciences > HG Finance |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
| Depositing User: | A.Md.SI Indah Lestari Wulan Aji |
| Date Deposited: | 11 Jun 2024 03:03 |
| Last Modified: | 12 Jun 2024 04:35 |
| URI: | http://eprints.upnyk.ac.id/id/eprint/39731 |
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