KLASIFIKASI KEBUTUHAN JUMLAH PRODUK MAKANAN CUSTOMER MENGGUNAKAN K-MEANS CLUSTERING DENGAN OPTIMASI PUSAT AWAL CLUSTER ALGORITMA GENETIKA

Yudi Istianto, NIM. 18206050003 (2020) KLASIFIKASI KEBUTUHAN JUMLAH PRODUK MAKANAN CUSTOMER MENGGUNAKAN K-MEANS CLUSTERING DENGAN OPTIMASI PUSAT AWAL CLUSTER ALGORITMA GENETIKA. Masters thesis, UNIVERSITAS ISLAM NEGERI SUNAN KALIJAGA.

Preview	Text (KLASIFIKASI KEBUTUHAN JUMLAH PRODUK MAKANAN CUSTOMER MENGGUNAKAN K-MEANS CLUSTERING DENGAN OPTIMASI PUSAT AWAL CLUSTER ALGORITMA GENETIKA) 18206050003_BAB-I_V.pdf - Published Version Download (2MB) | Preview
	Text (KLASIFIKASI KEBUTUHAN JUMLAH PRODUK MAKANAN CUSTOMER MENGGUNAKAN K-MEANS CLUSTERING DENGAN OPTIMASI PUSAT AWAL CLUSTER ALGORITMA GENETIKA) 18206050003_BAB-II_sampai_BAB-IV.pdf - Published Version Restricted to Registered users only Download (3MB)

Abstract

PT.XYZ is one of the companies in Yogyakarta engaged in the production and distribution of bakery food products. Every consumer has an irregular amount of bread needs while bread can only last for two days. Bread that is more than two days old will be replaced by a new one by the distributor which causes losses for the company. This study tries to apply data mining to classify the number of customer needs for food products using k-means clustering with optimization initial cluster center genetic algorithm. In this study 210 data were used from product sales for three weeks. The data will be processed by applying the data mining method with preprocessing before going through the classification stage. Preprocessing includes data transformation and k-means clustering. The results of clustering that require certain rules are more effective with optimization because of 210 data there are 200 data that are worth entering the classification stage. The results of the test get the best accuracy of 58.50% and crossvalidation for five fold managed to get an average accuracy of 50.58% greater than 2.51% of KNN without preprocessing.

Share this knowledge with your friends :

Actions (login required)

View Item