Klasterisasi Data Tenaga Kerja Terbuka Menurut Provinsi Dengan Penggunaan Algoritma K-Means - Siti Norhasanah; Abdul Aziz (JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 2023)
This research aims to provide valid and accurate information to the government to make appropriate decisions and policies in overcoming open unemployment in provinces in Indonesia with high unemployment rates. With an effective and efficient allocation of resources, it is hoped that economic development and social welfare will increase. Information mining is an iterative and intelligent cycle of finding new examples or models, which really helps to make sense of huge data sets. Information mining that contains the search for patterns or ideal examples in very large data sets to help make future choices. To test the information that has been broken down. The following is an explanation of the test using the rapidminer 9.9 programming. The linkages obtained between manual calculation calculations and the results to be shown by the rapidminer instrument and it is quite possible to reason that the consequences of manual estimation from the K-Means technique imply and from the Microsoft excel 2019 programming application are equivalent to information into the Rapidminer application. Research using data from 34 provinces shows that the k-means clustering method is successfully applied in data mining. Provincial unemployment data from 2019 to 2022 is used as the data source. The results of the analysis show that there are 2 clusters, namely 23 provinces with high unemployment cluster scores and 11 provinces with low unemployment cluster scores. That way the government can expand employment and overcome the problem of unemployment in the region.