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Muhammad Suherman
Univerisitas Bina Sarana Informatika
Computer Science & IT

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Penerapan Metode K-Means Clustering pada Data Tingkat Pengangguran Terbuka Tahun 2016-2018 dan 2019-2021 Rayhan Maliqi; Kursehi Falgenti; Sinta Priani; Fajrul Fithri; Muhammad Suherman; Dwi Satria Nugraha
Computer Science (CO-SCIENCE) Vol. 2 No. 2 (2022): Juli 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v2i2.1151

Abstract

The problem of unemployment has an impact on poverty, crime, and inequality in living standards. The government needs to anticipate these impacts through various policies. Knowledge is essential in supporting decision-making and policy formulation related to unemployment. Researchers have been mining data to gain new knowledge from Indonesia's Open Unemployment Rate (TPT) data. Continuous exploration of data has the opportunity to gain new knowledge. This study aims to mine Indonesian TPT data from 2016 to 2021. More specifically, look at changes in the 2016-2018 TPT data cluster with the 2019-2021 TPT data cluster. This research is a clustering analysis research using the k-means algorithm. The stages of clustering research consist of 1) data collection, 2) pre-processing, 3) determination of the optimal number of clusters, 4) data clustering with the k-mean, and 5) interpretation of the results of clustering. Based on the k-means clustering analysis of 2016-2018 and 2019-2021 TPT data, there are 23 provinces in cluster 1 (low TPT) and 11 provinces in cluster 2 (high TPT). Only Riau Province rose to cluster 1 (low TPT), and only West Sumatra province dropped to cluster 2 (High TPT). The study's results not only yielded the number of provinces in the high and low clusters but also found out which provinces moved to a higher cluster or vice versa in each TPT data.
Co-Authors Dwi Satria Nugraha Fajrul Fithri Kursehi Falgenti Rayhan Maliqi Sinta Priani