COMMUNITY COMPLAINTS GROUPING SYSTEM USING K-MEANS ALGORITHM INMORODEMAK DISTRICT

Abstract

Morodemak sub-district is facing various community problems that require quick and precise handling from the
sub-district. Management of public complaints is currently still done manually, causing a buildup of unresolved
complaints and reducing the level of citizen satisfaction. This manual syste m is unable to group complaints based
on problem type, level of urgency, and geographic location, making analysis of complaint data difficult. To
overcome this problem, this research proposes the application of the K -Means algorithm to automatically group
public complaints. The aim of this research is to increase the efficiency and effectiveness of complaint
management, facilitate data analysis, and increase the responsiveness and accountability of public services in
Morodemak Village. Through interview methods with sub-district officials and the community, this research will
collect the data needed to design and implement an innovative complaint grouping system. Based on the results of
the analysis, three categories were obtained. Cluster 1 had the lowest population value, Cluster 2 had the average
population value, Cluster 3 had the higest population value.