Kluster Rata-Rata Lama Sekolah (RLS) Menurut Jenis Kelamin di Provinsi Jawa Tengah dengan K-Means

Abstract

The average length of school (RLS) describes the level of achievement in school activities of each citizen in a given area. The higher the number of years of schooling, the higher the population's level of education, hence this indicator is critical since it can reveal the quality of a region's human resources. Furthermore, numerous studies have found that the average length of schooling has a major impact on economic growth. This indicates that when the average length of schooling rises, the number of unemployed and poor people in a given area declines, resulting in a positive and considerable impact on economic growth. The goal of the study was to use artificial intelligence to undertake an analysis in the form of a cluster mapping of the Average Length of Schooling in Regencies and Cities in Central Java (AI). This is necessary in order to gain a macro picture of the average years of schooling's progress over the last few years through regional mapping. The data was taken from the Central Java Statistics Agency (BPS) website, and it was based on the subject Average Length of School (RLS) by gender from 2017 to 2019. The k-means method, which is part of clustering data mining, was employed as the solution method. There were two types of clusters used in this study: high and low clusters. RapidMiner software is used to aid the analyzing process. Preprocessing is done before the k-means approach by taking the average value of the number of RLS based on gender from 2017 to 2019. K-means will be used to process the results of the average value obtained. According to the findings, eight provinces (23 percent) were in the high cluster (cluster 1) while 27 provinces (77 percent) were in the low cluster (cluster 0). According to the findings, RLS levels are still low in over 70% of Central Java's locations.