PENERAPAN DATA MINING PADA PENJUALAN PAKAIAN MENGGUNAKAN METODE K-MEANS CLUSTERING (STUDI KASUS DI TOKO RISKI)

DEWI PRAMUDITA, NPM 2109100022 (2025) PENERAPAN DATA MINING PADA PENJUALAN PAKAIAN MENGGUNAKAN METODE K-MEANS CLUSTERING (STUDI KASUS DI TOKO RISKI). Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini dilatarbelakangi oleh kebutuhan Toko Riski dalam memanfaatkan data penjualan yang selama ini hanya digunakan untuk pencatatan sederhana, tanpa dianalisis lebih lanjut, sehingga dengan penerapan metode Data Mining, khususnya K-Means Clustering, diharapkan pola pembelian pelanggan dapat tergali untuk mendukung strategi bisnis yang lebih tepat sasaran. K-Means Clustering sendiri merupakan salah satu algoritma unsupervised learning yang bertujuan mengelompokkan data ke dalam beberapa klaster berdasarkan kemiripan karakteristik, dan dalam penelitian ini digunakan untuk mengidentifikasi segmen pelanggan berdasarkan pola belanja pada data transaksi penjualan pakaian di Toko Riski. Proses analisis dilakukan dengan mengumpulkan data transaksi penjualan, melakukan transformasi dari data kategorikal ke numerik, lalu menghitung jaraknya dengan centroid untuk membentuk klaster pelanggan yang memiliki karakteristik serupa. Hasil pengelompokan menunjukkan adanya tiga klaster pelanggan, yaitu pelanggan dengan belanja rutin bernilai rendah, pelanggan dengan pembelian sedang, serta pelanggan premium dengan nilai transaksi tinggi, yang selanjutnya dapat dimanfaatkan oleh Toko Riski untuk menyusun strategi promosi, pengelolaan stok, dan pelayanan yang berbeda sesuai karakteristik tiap klaster. Dengan demikian, penerapan metode K Means Clustering terbukti membantu Toko Riski dalam mengenali pola belanja pelanggan serta segmentasi pasar yang lebih jelas, sehingga hasil analisis ini dapat meningkatkan efektivitas strategi bisnis, mengoptimalkan stok, dan memperkuat kepuasan pelanggan. Kata Kunci: Data Mining, K-Means Clustering, Pola Belanja Pelanggan, Segmentasi Pasar, Penjualan Pakaian ================================================================================================= This research is motivated by the need of Toko Riski to utilize sales data that has so far only been used for simple recording, without further analysis, so that by applying Data Mining methods, especially K-Means Clustering, it is expected that customer purchasing patterns can be explored to support more targeted business strategies. K-Means Clustering itself is one of the unsupervised learning algorithms that aims to group data into several clusters based on similar characteristics, and in this study it is used to identify customer segments based on shopping patterns in clothing sales transaction data at Toko Riski. The analysis process is carried out by collecting sales transaction data, transforming it from categorical to numeric data, then calculating the distance from the centroid to form customer clusters that have similar characteristics. The Clustering results show the existence of three customer clusters, namely customers with low-value routine purchases, customers with medium purchases, and premium customers with high transaction values, which can then be utilized by Toko Riski to develop different promotional strategies, stock management, and services according to the characteristics of each cluster. Thus, the application of the K-Means Clustering method has been proven to help Toko Riski in recognizing customer shopping patterns and clearer market segmentation, so that the results of this analysis can increase the effectiveness of business strategies, optimize stock, and strengthen customer satisfaction. Keywords: Data Mining, K-Means Clustering, Customer Shopping Patterns, Market Segmentation, Clothing Sales

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Data Mining, K-Means Clustering, Pola Belanja Pelanggan, Segmentasi Pasar, Penjualan Pakaian===============Data Mining, K-Means Clustering, Customer Shopping Patterns, Market Segmentation, Clothing Sales
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	25 Nov 2025 04:20
Last Modified:	25 Nov 2025 04:20
URI:	http://repository.ulb.ac.id/id/eprint/1997

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