Open AccessIMPLEMENTASI K-MEANS CLUSTERING PADA PENGELOMPOKAN POTENSI KERJASAMA PELANGGAN
Author(s) -
Ragil Prasojo,
Yustina Retno Wahyu Utami,
Retno Tri Vulandari
Publication year - 2019
Publication title -
jurnal teknologi informasi dan komunikasi sinar nusantara
Language(s) - English
Resource type - Journals
eISSN - 2620-7532
pISSN - 2338-4018
DOI - 10.30646/tikomsin.v7i2.435
Subject(s) - cluster analysis , computer science , competition (biology) , cluster (spacecraft) , data mining , business , marketing , artificial intelligence , ecology , biology , programming language
Tight competition in the business world today, the number of MSMEs engaged in the same field, this requires MSMEs to develop strategies to achieve goals. Apart from having to develop products and services, an MSME must also retain customers. Therefore grouping of potential customers is needed. By utilizing data that is an indicator of the customer. This utilization is called data mining. Data mining is run based on data that has been determined that is customer data, number of accessories, cooperation time, and item returns. Therefore in this study, a potential customer collaboration system was designed using the K-Means method, so that potential customers are obtained. The results of this study are a web-based system application that can classify customers with the K-Means method. Grouping into 3 clusters, the first cluster with enough criteria consists of 7 customer data. This criterion consists of customers who have a small number of goods purchased and a large number of goods returned. The second cluster consists of 17 customer data with good criteria. This criterion consists of customers who have a large number of goods purchases and a few goods returns. The third cluster consists of 7 customer data with very good criteria. This criterion consists of customers who have the most number of purchases and the least return of goods.Keywords: customer, Data Mining, K-Means Clustering