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, 2025)
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.