PRIYA WARDANA, NPM 2209100100 (2026) ANALISIS POLA PEMBELIAN MENGGUNAKAN ALGORITMA K-MEANS DAN APRIORI PADA CAFE LEGA. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Data transaksi penjualan pada usaha kuliner dapat dimanfaatkan untuk mengetahui kecenderungan pembelian pelanggan dan mendukung pengambilan keputusan bisnis. Penelitian ini bertujuan menganalisis pola pembelian pelanggan pada Cafe Lega menggunakan algoritma K-Means dan Apriori. Data yang digunakan berupa 31 transaksi penjualan selama bulan Desember 2025 yang mencakup nama produk, jumlah item, total pembelian, dan kombinasi produk dalam setiap transaksi. Tahapan penelitian meliputi pengumpulan data, pembersihan data, reduksi data, transformasi data, penerapan algoritma K-Means, penerapan algoritma Apriori, serta analisis dan interpretasi hasil. Algoritma K-Means digunakan untuk mengelompokkan transaksi ke dalam tiga klaster, sedangkan algoritma Apriori digunakan untuk menemukan hubungan antarproduk dengan minimum support sebesar 30% dan minimum confidence sebesar 60%. Hasil K-Means menunjukkan bahwa klaster transaksi rendah terdiri atas 6 transaksi atau 19,35%, klaster transaksi menengah terdiri atas 16 transaksi atau 51,61%, dan klaster transaksi tinggi terdiri atas 9 transaksi atau 29,03%. Hasil Apriori menunjukkan bahwa kombinasi Thai Green Tea dan Matcha Latte memiliki nilai support sebesar 32,26%. Aturan Thai Green Tea menuju Matcha Latte menghasilkan confidence sebesar 100%, sedangkan aturan Matcha Latte menuju Thai Green Tea menghasilkan confidence sebesar 71,43%. Kombinasi kedua algoritma tersebut mampu memberikan gambaran mengenai segmentasi transaksi dan hubungan pembelian antarproduk. Hasil penelitian dapat digunakan sebagai dasar dalam menentukan produk unggulan, menyusun promosi paket, mengelola persediaan bahan baku, dan meningkatkan strategi penjualan Cafe Lega. Kata Kunci: Algoritma Apriori, Algoritma K-Means, Data Mining, Pola Pembelian, Transaksi Penjualan ================================================================================================= Sales transaction data in culinary businesses can be utilized to identify customer purchasing tendencies and support business decision-making. This study aims to analyze customer purchasing patterns at Cafe Lega using the K-Means and Apriori algorithms. The research used 31 sales transactions recorded during December 2025, consisting of product names, the number of items, total purchases, and product combinations within each transaction. The research stages included data collection, data cleaning, data reduction, data transformation, implementation of the K-Means algorithm, implementation of the Apriori algorithm, and analysis and interpretation of the results. The K-Means algorithm was applied to classify transactions into three clusters, while the Apriori algorithm was used to identify relationships between products using a minimum support of 30% and a minimum confidence of 60%. The K-Means results showed that the low-transaction cluster consisted of 6 transactions or 19.35%, the medium-transaction cluster consisted of 16 transactions or 51.61%, and the high-transaction cluster consisted of 9 transactions or 29.03%. The Apriori analysis revealed that the combination of Thai Green Tea and Matcha Latte had a support value of 32.26%. The Thai Green Tea to Matcha Latte association rule produced a confidence value of 100%, while the Matcha Latte to Thai Green Tea rule produced a confidence value of 71.43%. The combination of these algorithms provides a comprehensive overview of transaction segmentation and product-purchasing relationships. The findings can support Cafe Lega in determining its featured products, designing product-bundling promotions, managing raw-material inventory, and improving sales strategies. Keywords: Apriori algorithm, K-Means Algorithm, Data Mining, Purchasing Patterns, Sales Transactions
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Algoritma Apriori, Algoritma K-Means, Data Mining, Pola Pembelian, Transaksi Penjualan===============Apriori algorithm, K-Means Algorithm, Data Mining, Purchasing Patterns, Sales Transactions |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) 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: | 03 Aug 2026 04:33 |
| Last Modified: | 03 Aug 2026 04:33 |
| URI: | http://repository.ulb.ac.id/id/eprint/2625 |
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