PENGELOMPOKAN PELANGGAN BERDASARKAN PREFERENSI BELANJA DI TOKO TRIKO RANTAUPRAPAT DENGAN K-MEANS CLUSTERING

FATIMAH ZAHRA RAMBE, NPM 2109100097 (2025) PENGELOMPOKAN PELANGGAN BERDASARKAN PREFERENSI BELANJA DI TOKO TRIKO RANTAUPRAPAT DENGAN K-MEANS CLUSTERING. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini mengeksplorasi penerapan algoritma K-Means Clustering untuk mengelompokkan pelanggan Toko Sepatu Triko di Rantauprapat berdasarkan preferensi belanja mereka. Dalam konteks persaingan ritel yang semakin ketat, pemahaman atas perilaku dan preferensi pelanggan diharapkan dapat meningkatkan loyalitas dan volume penjualan. Metode ini bertujuan untuk mengidentifikasi pola belanja dan segmentasi pelanggan ke dalam tiga cluster: pelanggan pasif, netral, dan potensial. Melalui evaluasi model menggunakan Elbow Method, Davies-Bouldin Index, dan Silhouette Coefisien, hasil menunjukkan bahwa jumlah cluster optimal adalah tiga. Cluster pertama terdiri dari pelanggan dengan frekuensi rendah dan jumlah transaksi, mewakili pelanggan pasif. Cluster kedua menggambarkan pelanggan netral dengan perilaku belanja sedang. Cluster ketiga mencakup potensi pelanggan, yang aktif berbelanja dan memberikan kontribusi signifikan pada penjualan. Implementasi K-Means, yang dilaksanakan dengan bantuan Python dan Google Colab, menunjukkan efektivitasnya dalam menyediakan data analisis terstruktur dan mendalam terhadap karakteristik pelanggan. Temuan ini memberikan strategi pemasaran yang lebih tepat sasaran seperti promosi untuk meningkatkan loyalitas pelanggan, serta upaya untuk membangkitkan kembali pelanggan pasif. Penelitian ini menggarisbawahi pentingnya pengelolaan data pelanggan yang terstruktur dalam mendukung keputusan strategi di industri ritel. Kata Kunci: K-Means Clustering, Segmentasi Pelanggan, Preferensi Belanja, Toko Sepatu Triko, Data Mining, Strategi Pemasaran =================================================================================================== This study explores the application of the K-Means Clustering algorithm to group customers of Triko Shoe Store in Rantauprapat based on their shopping preferences. In the context of increasingly fierce retail competition, understanding customer behavior and preferences is expected to increase loyalty and sales volume. This method aims to identify shopping patterns and segment customers into three clusters: passive, neutral, and potential customers. Through model evaluation using the Elbow Method, Davies-Bouldin Index, and Silhouette Coefficient, the results indicate that the optimal number of clusters is three. The first cluster consists of customers with low frequency and transaction volume, representing passive customers. The second cluster describes neutral customers with moderate shopping behavior. The third cluster includes potential customers who are active shoppers and contribute significantly to sales. The implementation of K-Means, carried out with the help of Python and Google Colab, demonstrates its effectiveness in providing structured and in-depth analysis of customer characteristics. These findings provide more targeted marketing strategies, such as promotions to increase customer loyalty, as well as efforts to reactivate passive customers. This research underscores the importance of structured customer data management in supporting strategic decisions in the retail industry. Keywords: K-Means Clustering, Customer Segmentation, Shopping Preferences, Triko Shoe Store, Data Mining, Marketing Strategy

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	K-Means Clustering, Segmentasi Pelanggan, Preferensi Belanja, Toko Sepatu Triko, Data Mining, Strategi Pemasaran================K-Means Clustering, Customer Segmentation, Shopping Preferences, Triko Shoe Store, Data Mining, Marketing Strategy
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	03 Sep 2025 03:12
Last Modified:	03 Sep 2025 03:12
URI:	http://repository.ulb.ac.id/id/eprint/1672

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