ABIYMANYU, ALVITO DIMAS (2024) ANALISIS KLASTERISASI PADA DATA PENJUALAN COFFEE SHOP GUNA MEMPREDIKSI STOK BARANG DENGAN MENGGUNAKAN ALGORITMA K-MEANS. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Recently, coffee shops have become a favorite spot for people of all ages. Therefore, coffee shops need to analyze and understand their sales data. However, due to the large volume of sales data, this process becomes challenging. This issue can be resolved by clustering coffee sales data. Clustering is the process of grouping data into several clusters based on data similarity. In this study, the author used data including variables such as item name, item price, sales date, and sales quantity. The data used consisted of 2181 coffee sales transactions with various types of coffee sold and varying sales quantities. In this research, data mining is applied using the K-Means process model, which offers a standard process for using data mining across various fields and is chosen because its results are easy to understand and interpret. The findings of this study have a positive impact on increasing the productivity of the coffee shop industry and contribute to the growth of knowledge in the field of business management analysis. The results of this research are expected to provide more accurate information regarding customer service in coffee shops and help in developing more effective marketing strategies. Keywords: Clustering analysis, Coffee shop, Sales data, K-Means algorithm, Marketing strategy. Belakangan ini, coffee shop menjadi tempat favorit bagi berbagai kalangan. Oleh karena itu, coffee shop perlu menganalisis dan memahami data penjualannya. Namun, karena banyaknya data penjualan yang ada, proses ini menjadi sulit. Masalah tersebut dapat diatasi dengan melakukan klasterisasi data penjualan kopi. Klasterisasi adalah proses pengelompokan data menjadi beberapa cluster berdasarkan tingkat kesamaan data. Dalam penelitian ini, penulis menggunakan data yang mencakup variabel seperti nama item, harga item, tanggal penjualan, dan kuantitas penjualan. Data yang digunakan berjumlah 2181 transaksi penjualan kopi dengan berbagai jenis kopi dan variasi jumlah penjualannya. Pada penelitian ini, data mining diterapkan dengan menggunakan model proses K-Means, yang menawarkan proses standar untuk penggunaan data mining di berbagai bidang, dan dipilih karena hasil metodenya mudah dipahami dan diinterpretasikan. Temuan penelitian ini memiliki dampak positif terhadap peningkatan produktivitas industri coffee shop dan berkontribusi terhadap perkembangan pengetahuan di bidang analisis manajemen bisnis. Hasil penelitian ini diharapkan dapat memberikan informasi yang lebih akurat mengenai layanan pelanggan di coffee shop dan membantu dalam mengembangkan strategi pemasaran yang lebih efektif. Kata kunci: Analisis klasterisasi, Coffee shop, Data penjualan, Algoritma KMeans, Strategi pemasaran.
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