SETIAWAN, ERIC (2022) ANALISIS SEGMENTASI PELANGGAN CINEMA BOOKING BIOSKOP XYZ DAN ASSOCIATION RULES PEMESANAN MAKANANNYA. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The rapid development of technology makes competition in various business fields getting tighter. In a business that deals with customers, a strategy is needed to be able to make customers remain royal. Companies are required to make data-driven decisions for formulating the right strategy to maintain good relationships with customers. To maintain this good relationship, it is necessary to understand the behavior or habits of customers based on transaction history. Data mining becomes one of the solutions. K-Means clustering algorithm can be used to divide customers into several groups based on their characteristics. Eclat's association rules algorithm can be used to find out the pattern of customer habits in buying food packages. Based on the K-Means algorithm, 3 clusters are obtained. Cluster 0 as bronze customer, cluster 1 as gold customer, and cluster 2 as silver customer. From gold customers, the results of association rules using a minimum support value of 1 % and a minimum confidence value of 75% using Eclat algorithm are 5 rules. Popcorn Sweet Glaze Medium, Popcorn Salt Medium, and Mineral Water are the results of the rules with the highest confidence value of 79,59%. Key words: Customer Segmentation, RFM, Clustering, Association Rules, Data Mining Perkembangan teknologi yang pesat membuat persaingan diberbagai bidang bisnis menjadi semakin ketat. Dalam bisnis yang berhubungan dengan pelanggan, dibutuhkan strategi untuk dapat membuat pelanggan tidak berpaling ke kompetitor. Perusahaan diharuskan mengambil keputusan berdasarkan data-driven dalam penyusunan strategi yang tepat untuk dapat menjaga hubungan baik dengan pelanggan. Untuk menjaga hubungan baik tersebut, diperlukan pemahaman perilaku atau kebiasaan yang dilakukan pelanggan berdasarkan histori transaksi. Data mining menjadi salah satu solusi untuk dapat melakukan hal tersebut. Algoritma clustering K-Means dapat digunakan untuk membagi pelanggan kedalam beberapa grup berdasarkan karakteristiknya. Algoritma association rules Eclat dapat digunakan untuk mengetahui pola kebiasaan pelanggan dalam membeli paket makanan. Berdasarkan algoritma K-Means didapatkan hasil 3 cluster. Cluster 0 sebagai cluster pelanggan bronze, cluster 1 sebagai cluster pelanggan gold, dan cluster 2 sebagai cluster pelanggan silver. Dari pelanggan gold didapatkan hasil association rules menggunakan algoritma Eclat sebanyak 5 rules dengan nilai minimal support 1% dan nilai minimal confidence 75%. Popcorn Sweet Glaze Medium, Popcorn Salt Medium, dan Mineral Water merupakan hasil rules dengan nilai confidence tertinggi yaitu 79,59%. Kata kunci: Segmentasi Pelanggan, RFM, Clustering, Association Rules, Data Mining
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