Penerapan Metode K-Means Pada Pengelompokkan Pengangguran Di Indonesia

Abstract

Unemployment is a group of labor force who has not done an activity that generates money. Someone who is said to be unemployed can also be categorized as people who have not worked, people who are looking for work, or people who have worked but have not gotten productive results. The purpose of this study is to analyze the unemployment stay by province in Indonesia. This research data is sourced from the Central Statistics Agency in 2014 - 2019. This study uses data mining techniques, namely the K-means algorithm, the K-means method is a clustering method that functions to break the dataset into groups. The K-means method can be used for percentage unemployment data by province. Data will be divided or grouped into 2 Clusters, where Cluster 1 is the group of provinces with the highest potential for unemployment with the results of 13 provinces and Cluster 2 is the province with the lowest potential unemployment results which is 21 provinces. The results of this study are as a way to assist the government in expanding employment to develop and improve the economy in each province in Indonesia. It is hoped that this research can provide input to the government. In particular, the provinces with minimal employment opportunities in Indonesia have an impact on unemployment