K-Means Method for Clustering Average Length of School (ALS) in Central Java

Abstract

Average School Length (ALS) represents the level of performance of every person in a school region. The more years of education, the better the education obtained by the population; therefore, it becomes more essential as it can demonstrate the quality of the human resources in a given region. In addition, extensive research implies that the average length of schooling influences economic growth considerably. If the average length of education improves, then the number of unemployed and poor in an area decreases and affects economic growth positively and significantly. The study’s goal was to carry out an investigation utilizing artificial intelligence in the form of a cluster mapping on the averages of regeneration in central Java. This needs to be done to get a macro view of the level of development of the average school years through regional mapping over the last few years. The data set used can be found on the website of the Central Java Provincial Statistics Agency, which is the subject of the 2017-2019 average school year by sex. The solution approach is k-means, which is part of the data collection process. High and low clusters are the number used in this investigation. Prior to the k-means approach, pre-processing is conducted out by taking from 2017-2019 the average RLS number based on gender. The results of the computed average value were analyzed with k-means. The study found that out of 35 provinces, eight (23%) provinces were of the high cluster (cluster 1) and 27 of the low cluster (cluster 0) (77%). More than 70% of Central Java regions have remained common ALS, according to results. Keywords: K-Means, clustering, ALS