Clustering of Accounts Receivable Billing Data Based on Customer Tariff Categories at PT PLN UP3 Palembang

Ramadhan, Dimaz Gymnastiar
Yulistia, Yulistia

Journal Of Artificial Intelligence And Software Engineering
Vol 5, No 2 (2025): Juni On-Progress

The purpose of writing this final assignment is to group customers based on late payment patterns by applying the K-Means Clustering algorithm. The data used are late receivables and arrears of PT PLN Palembang customers. The results of writing this final assignment show that Cluster 1 has 10 data, Cluster 2 has 36 data, and Cluster 3 has 326 data on late payments. While in the risky payment arrears, Cluster 1 has 26 data, Cluster 2 has 36 data, and Cluster 3 has 312 data. From the evaluation results using Silhouette Score, it shows that there are 3 clusters with a value of 0,880 (Highest), which means that the clustering that was formed was successful and can be used.