Penelitian ini bertujuan untuk menganalisis perubahan pola mobilitas non-permanen tenaga kerja di kawasan metropolitan antara sebelum dan selama pandemi Covid-19. Penelitian ini juga menganalisis pengaruh faktor individu dan faktor daerah asal dan tujuan pekerja terhadap keputusan pilihan pola mobilitas non-permanen selama pandemi. Adapun data yang digunakan adalah Sakernas 2019, 2020, dan 2021. Metode penelitian menggunakan regresi multinomial logistik. Hasil penelitian menunjukan terjadi penurunan pola mobilitas non permanen di kawasan metropolitan Indonesia selama pandemi melanda. Faktor yang mempengaruhi pola mobilitas komuter selama pandemi adalah usia, jenis kelamin, tingkat pendidikan, status pekerjaan, sektor lapangan pekerjaan, klasifikasi daerah tempat tinggal, serta pertumbuhan PDRB. Sementara, faktor yang mempengaruhi pola mobilitas sirkuler selama pandemi melanda adalah usia, jenis kelamin, status perkawinan, tingkat pendidikan, status pekerjaan, bekerja di sektor manufaktur, klasifikasi daerah tempat tinggal, serta pertumbuhan PDRB.

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This research aims to analyze the changes in the patterns of non-permanent labor mobility in metropolitan areas before and during the Covid-19 pandemic. The study also examines the influence of individual factors and factors related to the workers' origin and destination areas on the decision to choose non-permanent mobility patterns during the pandemic. The study utilizes data from Sakernas (National Labor Force Survey) for the years 2019, 2020, and 2021. The research methodology employed in this study is multinomial logistic regression. The findings of this research reveal a notable decline in non-permanent mobility patterns in Indonesian metropolitan areas during the pandemic. Factors that influence commuter mobility patterns during the pandemic include age, gender, education level, employment status, employment sector, residential area classification, and regional gross domestic product growth. Meanwhile, factors that affect circular mobility patterns during the pandemic include age, gender, marital status, education level, employment status, working in the manufacturing sector, residential area classification, and regional GDP growth.