Clustering of Community Complaints on Flooding and Waterlogging Using the K-Means Clustering Method at the Public Works and Spatial Planning Agency

Rahmawati Dewi (STMIK KAPUTAMA)
Magdalena Simanjuntak (STMIK KAPUTAMA)
I Gusti Prahmana (STMIK KAPUTAMA)

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 6 No. 1 (2026): October 2026

The Department of Public Works and Spatial Planning (PUTR) plays an important role in infrastructure development, which has a significant impact on regional economic growth. Flooding and waterlogging problems often occur due to high rainfall and suboptimal drainage systems, resulting in losses and disruptions to community activities.This study aims to classify public complaints related to flooding and waterlogging using the K-Means Clustering method. The data used consists of 617 public complaints, which are grouped into three clusters. The results show that Cluster 1 contains 355 data points, Cluster 2 contains 94 data points, and Cluster 3 contains 168 data points. This clustering is expected to assist PUTR in understanding patterns of public complaints and in determining more effective and targeted handling priorities.