IMPLEMENTATION OF THE K-MEANS CLUSTERING ALGORITHM IN ANALYZING PUBLIC SATISFACTION REGARDING PUBLIC SERVICES (STUDI CASE: BALAI PENGUJIAN STANDAR INSTRUMEN TANAMAN INDUSTRI DAN PENYEGAR)

With the development of today's modern era, publik service is an important and very necessary thing because it is one of the benchmarks for seeing publik trust and satisfaction with the services provided by an agency. One of the agencies that carries out publi services is the Balai Pengujian Standar Instrumen Tanaman Industri dan Penyegar (BPSI TRI), a government agency under the Ministry of Agriculture. There are a lot of people who will receive services in 2023. Therefore, publik service officers find it difficult to determine publik satisfaction in order to optimize the services provided. To determine community satisfaction, 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) as well as handling complaints, suggestions and input (U8) then carried out calculations using Microsoft Excel and got the results that C1 (unsatisfactory) 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.