PENERAPAN ALGORITMA K-MEANS CLUSTERING PADA DATA KELUHAN PELANGGAN PT. PLN PERSERO KOTA PAGAR ALAM - Sasmita Sasmita; Siti Muntari (Universitas PGRI Semarang, 2023)
In this modern era, all activities and needs of residents are largely influenced by electricity. Electricity is needed because all household appliances use electric power for company needs or residential needs. To improve the service quality of PT. PLN Persero, Pagar Alam City, in order to reduce the number of customer complaints in Pagar Alam City, a data clustering process is needed which is very important because the increase in data is quite significant. The process of grouping data uses K-Means Clustering because this algorithm is suitable for grouping the data. The results of this study are in the form of designing the application of the K-Means Clustering algorithm to customer complaint data at PT.PLN Persero City of Pagar Alam where the cluster is divided into 3, namely C0 as the highest complaint level, C1 for moderate complaint levels and C2 for low complaint levels. The data import process uses rapidminner. From the pattern obtained in rapidminer which is used on the system with the k-means clustering method, it is obtained that cluster_0 has a high complaint rate with a total of 108, cluster_1 has a moderate complaint rate with a total of 75 and cluster_2 has a low complaint rate with a total 45.