Penerapan K-Means Clustering dan Cross-Industry Standard Process For Data Mining (CRISP-DM) untuk Mengelompokan Penjualan Kue

Muhammad Rafi Muttaqin (Sekolah Tinggi Teknologi Wastukancana)
Teguh Iman Hermanto (Sekolah Tinggi Teknologi Wastukancana)
Muhamad Agus Sunandar (Sekolah Tinggi Teknologi Wastukancana)

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Vol 19, No 1 (2022): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika

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.