Proceedings of the 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019)

The Grouping of Regions are Based on the Unemployment Rate in the Attacking Districts with the K-Means Method

Authors
Rudianto, Ramdani Budiman
Corresponding Author
Rudianto
Available Online 6 March 2020.
DOI
10.2991/assehr.k.200303.043How to use a DOI?
Keywords
idleness, clustering, k-means method, data mining
Abstract

Each region experiences the number of idleness faced by the government. Currently, the Government through the Central Bureau of Statistics conducts a National Labor Force Survey to determine the number of unemployed from each region. Banten Province experienced a decline in the Labor Force Participation Rate (LFPR) of 1.60 percent. Currently, the open idleness grouping conducted in Serang District still uses group strata sourced from the office of the Provincial Central Bureau of Statistics. Based on the data already in the group BPS only displays idleness data based on certain criteria and does not show which region is the highest idleness rate. Therefore, it is necessary to classify the region to know which areas have a high and low idleness rate. In this case, the researcher collects data by looking at data already collected by BPS, by interviewing and viewing literature on idleness data in Banten Province. After viewing and collecting idleness data the idleness grouping of open idleness can still be done in another way, namely to see the proximity of the data point distance between one indicator with other indicators, one of them using clustering approach by using k-means method. K-means method is a non-hierarchical clustering method that seeks to partition existing data into one or more forms. By using the method k-means aim in facilitating the grouping of a region by looking at the number of low idleness rates or high level which results is a pie chart that describes the number of areas that have been grouped based on calculations by the k-means method. From the results of the image can be easily seen in the area where the highest and lowest idleness.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
6 March 2020
ISBN
10.2991/assehr.k.200303.043
ISSN
2352-5398
DOI
10.2991/assehr.k.200303.043How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Rudianto
AU  - Ramdani Budiman
PY  - 2020
DA  - 2020/03/06
TI  - The Grouping of Regions are Based on the Unemployment Rate in the Attacking Districts with the K-Means Method
BT  - Proceedings of the 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019)
PB  - Atlantis Press
SP  - 177
EP  - 185
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200303.043
DO  - 10.2991/assehr.k.200303.043
ID  - 2020
ER  -