Clusterisasi Tingkat Pengangguran Terbuka Menurut Provinsi di Indonesia Menggunakan Algoritma K-Medoids

The Open Unemployment Rate (OER) in Indonesia decreased in February 2024 to 4.82%, showing an improvement compared to February 2023. Despite the decline in TPT, there are still regions with TPT reaching 7.02%, which could potentially lead to negative consequences such as increased crime. Efforts to address TPT include increasing economic growth, developing the quality of education and training. This research utilises clustering in data mining. The number of clusters formed was 3 clusters with a DBI value of -1.685. This study uses K-Medoids clustering to group 38 provinces based on TPT. Of the 38 data, there is incomplete data so preprocessing is done using the "filter example" operator in rapidminer to eliminate incomplete data so that there are 34 data that will be used in this study (after preprocessing). The results show 4 provinces with the highest TPT (Riau Islands, DKI Jakarta, West Java, and Banten) with a percentage of 11.76%.

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Publication Details

Journal
Building of Informatics Technology and Science (BITS)
Published
2024-12-03
DOI
https://doi.org/10.47065/bits.v6i3.6198
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Clusterisasi Tingkat Pengangguran Terbuka Menurut Provinsi di Indonesia Menggunakan Algoritma K-Medoids

Shinta Esabella, Abdul Karim, Erwin Mardinata, Kusmanto Kusmanto et al.
Building of Informatics Technology and Science (BITS)
Data Mining and Machine Learning Applications
article

Clusterisasi Tingkat Pengangguran Terbuka Menurut Provinsi di Indonesia Menggunakan Algoritma K-Medoids

Shinta Esabella, Abdul Karim, Erwin Mardinata, Kusmanto Kusmanto, Sudi Suryadi
article en

Abstract

The Open Unemployment Rate (OER) in Indonesia decreased in February 2024 to 4.82%, showing an improvement compared to February 2023. Despite the decline in TPT, there are still regions with TPT reaching 7.02%, which could potentially lead to negative consequences such as increased crime. Efforts to address TPT include increasing economic growth, developing the quality of education and training. This research utilises clustering in data mining. The number of clusters formed was 3 clusters with a DBI value of -1.685. This study uses K-Medoids clustering to group 38 provinces based on TPT. Of the 38 data, there is incomplete data so preprocessing is done using the "filter example" operator in rapidminer to eliminate incomplete data so that there are 34 data that will be used in this study (after preprocessing). The results show 4 provinces with the highest TPT (Riau Islands, DKI Jakarta, West Java, and Banten) with a percentage of 11.76%.

Building of Informatics Technology and Science (BITS)Vol. 6(3)
Universitas Al-washliyah Labuhanbatu (ID), Universitas Labuhanbatu (ID)
Openalex Percentile: Top 53%
Data Mining and Machine Learning Applications
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