Klasifikasi Data Penduduk Pada Pemilihan Umum Di Kota Binjai Menggunakan Algoritma K-Means (Studi Kasus : KPU Kota Binjai) - Windy Indah Sary Sinaga; Relita Buaton; Hermansyah Sembiring (Explorer, 2023)
Population growth is something that continues in an environment both in rural and urban areas. The rapidly increasing number of residents must be re-recorded in a government agency. Likewise, the Binjai City KPU Office must re-record population data, especially residents in the city of Binjai who have the right to carry out the General Election in 2024 by involving the community that has been previously recorded. Problems were also found with data on residents who had moved domiciles but their personal data had already been recorded for general elections in 2024. With that, data collection had to be re-done to select population data so as to produce a new population data status so that data was not found that did not match what it should be. By observing the problems above Data Mining with the Clustering method is very appropriate to be used to generate knowledge of new population data groups to carry out general elections at the KPU Binjai, using the MATLAB application is also very appropriate to choose in this problem so that it can produce output from data mining that can be used in future decision making. This study aims to process data to produce population data in the city of Binjai in the implementation of general elections, implement a system so that it can classify new population data in the middle of old population data and design data grouping in determining population data groups based on criteria conditions at the KPU Office in Binjai city. By using the clustering method that has been used to process population data at general elections in the city of Binjai, it can produce new information from 1000 data that has been tested. From 1000 population data for general elections in Binjai City, 3 clusters are obtained with the results of 7 tests where cluster 1 totals 225 data, cluster 2 has 436 data and cluster 3 has 339 data.