IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING DALAM MENGANALISIS KEPUASAN MASYARAKAT MENGENAI PELAYANAN PUBLIK (Studi Kasus: Balai Pengujian Standar Instrumen Tanaman Industri dan Penyegar)

With the current modern era, public service is one of the important things that is very necessary because it is one of the benchmarks to see the public's trust and satisfaction with the services provided by an agency, both business and government agencies. One of the agencies that provides public services is the Industrial and Refreshment Instrument Standards Testing Center (BPSI TRI), a government agency under the Ministry of Agriculture. In 2023, many people will receive services, therefore, public service officers will find it difficult to determine community satisfaction in order to optimize the services that will be provided. To find out this, data mining calculations were carried out using the K-Means clustering algorithm method with Community Satisfaction Index (IKM) data in 2023 using 3 (three) categories including unsatisfactory (C1), satisfactory (C2) and very satisfactory) and 2 attributes, namely the behavior of service officers (U7) and handling complaints, suggestions and input (U8) then carried out calculations using Microsoft Excel and got the results that C1 (unsatisfactory) was 14 respondents, C2 (satisfactory) 39 respondents and C3 (very satisfactory) 98 respondents. Meanwhile, from the results of calculations using Python testing, the results showed that C1 (unsatisfactory) was 9 respondents, C2 (satisfactory) was 39 respondents and C3 (very satisfactory) was 103 respondents.

Keywords: K-Means clustering, Microsoft Excel, Public Services, Python