SKRIPSI Sistem Informasi
PENGELOMPOKAN DATA PEANGGURAN DI INDONESIA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING
PENGELOMPOKAN DATA PENGANGGULRAN DI INDONESIA
MENGGULNAKAN ALGORITMA K-MEANS CLULSTERING
Novita Dwi Octaviana Ibrahlim1)
, Tulbaguls M. Akhlriza2)
, Rahlayul Widayant3)
STMIK PPKIA Pradnya Paramita Malang
novitadwioctaviana@gmail.com1)
, akhlriza@stimata.ac.id2)
, rahlayul@stimata.ac.id3)
Abstract
ULnemployment in Indonesia increased dulring 2019-2021 dule to COVID-19. Thle government
implemented PSBB to tackle thle viruls, bult thle economic downtulrn led to bulsiness efficiency
measulres and layoffs. Thlis stuldy analyzes ulnemployment data in Indonesian provinces ulsing KMeans Clulstering to see thle chlanges in ulnemployment rates in Indonesia before thle Covid-19 and
after thle Covid-19 pandemic in 2019 and 2021. Five clulsters were identified in 2019, whlile foulr
clulsters existed in 2021. Dulring 2019-2021, thle provinces of West Kalimantan, NTT, NTB, and
West Sullawesi moved from level 3 to level 1, indicating an increase in thle ulnemployment rate in
thlese areas. In addition, provinces sulchl as Soulthl Sullawesi, Malulkul, Northl Malulkul, Acehl, Northl Kalimantan, East Kalimantan, Banten, Jambi, Riaul, and Northl Sullawesi shlifted from level 5 to
level 3 indicating thlat thle area experienced an increase in thle ulnemployment rate, thlis also applied
to East Kalimantan, DKI Jakarta, and Riaul Islands thlat shlifted from level 5 to level 4. Thle
evalulation resullts shlow thlat 2019 produlces a silhloulette valule of 0.36 and 0.35 for 2021. Bothl silhloulette resullts are inclulded in thle weak clulster strulctulre criteria becaulse thle silhloulette valule is
in thle range of 0.26-0.50.
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