KOMPARASI KLASTER PENGANGGURAN TERBUKA DI INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN K-MEAN CLUSTERING

Raihan Maliqi; Kursehi Falgenti

doi:10.30998/faktorexacta.v16i1.15108

KOMPARASI KLASTER PENGANGGURAN TERBUKA DI INDONESIA SEBELUM DAN SAAT PANDEMI COVID-19 MENGGUNAKAN K-MEAN CLUSTERING

Raihan Maliqi⁽¹⁾, Kursehi Falgenti^(2*)

(1) Univrsitas Nusa Mandiri
(2) (Scopus ID 57189267771) Universitas Nusa Mandiri Jakarta
(*) Corresponding Author

Abstract

Open unemployment is a productive workforce of secondary education and higher education graduates who have not worked at all. The Indonesian Statistics Bureau (BPS) routinely publishes provincial open unemployment (TPT) data per semester. Many studies have analyzed TPT data by exploring new knowledge using data mining methods with cluster analysis techniques. Researchers have investigated the TPT data cluster under normal conditions before the COVID-19 pandemic. This study aims to find new knowledge from TPT data by comparing the TPT data cluster analysis results before the pandemic (2018-2019) with TPT data during the pandemic (2020-2021). Data mining techniques used are cluster analysis and the k-mean algorithm. The cluster analysis results are regional clusters with low and high unemployment rates before and during COVID-19. In addition, another finding is the movement from high to down clusters. Other interesting results are Covid-19 has the most impact on high unemployment in Aceh, North Sumatra, West Sumatra, Riau Islands, DKI Jakarta, West Java, Banten, East Kalimantan, North Sulawesi, Maluku, and West Papua. Anticipatory steps of the high open unemployment rates in 11 provinces, local government could design the program or policy that could support the BLT policy by the central government.

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DOI: http://dx.doi.org/10.30998/faktorexacta.v16i1.15108

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