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All Journal Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Muhamad Agus Sunandar
Sekolah Tinggi Teknologi Wastukancana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Penerapan K-Means Clustering dan Cross-Industry Standard Process For Data Mining (CRISP-DM) untuk Mengelompokan Penjualan Kue Muhammad Rafi Muttaqin; Teguh Iman Hermanto; Muhamad Agus Sunandar
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 19, No 1 (2022): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v19i1.3976

Abstract

Cake is a food that doesn’t have long durability. This will cause the cake producer to suffer a losses if the product is not sold out at the expiration date. With the availability of cake sales data, the sales potential will be clustered according to the date of sale using K-Means method. The data mining process used in this study is Cross-Industry Standard Process for Data Mining (CRISP-DM). The results obtained are the formation of agroup of cake sales that man consumers buy on each date. This grouping is divided into three, namely low, medium, and high sales. This will help producers to prepare their products more effectively and efficiently so as to reduce wasteful production. If the cake is in the low sales group, the number of cake products is small. On the contrary, if there is a cake that goes into high sales group, then the producer will produce the cake in large quantities.
Co-Authors Muhammad Rafi Muttaqin Teguh Iman Hermanto