Pemodelan Tingkat Pengangguran Terbuka Di Provinsi Sulawesi Utara Menggunakan Metode Regresi Data Panel

Sitanggang, Elizabeth Sondang Indah Maristela (2025) Pemodelan Tingkat Pengangguran Terbuka Di Provinsi Sulawesi Utara Menggunakan Metode Regresi Data Panel. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Tingkat Pengangguran Terbuka (TPT) merupakan indikator yang digunakan untuk menilai kesejahteraan sosial masyarakat untuk memantau perubahan pengangguran disetiap daerah. Selama tahun 2017 hingga 2024 TPT di Sulawesi Utara menunjukkan dinamika naik turun, dengan puncaknya mengalami kelonjakan pada tahun 2020 sebesar 7,37 persen diakibatkan COVID-19, kemudian mengalami penurunan secara bertahap hingga tahun 2024. Penelitian ini menggunakan empat variabel faktor yaitu PDRB sektor pertanian, PDRB sektor perdagangan, Tingkat Partisipasi Angkatan Kerja (TPAK), serta rata-rata lama sekolah. Penelitian ini menggunakan metode regresi data panel untuk memahami pengaruh lintas wilayah (kabupaten/kota) dan lintas waktu terhadap TPT. Model yang dihasilkan menggunakan varibael dummy untuk interaksi COVID-19. Pada penelitian ini menghasilkan model Fixed Effect Model (FEM) untuk estimasi model terbaik dalam menganalisis TPT dengan R^2 91 persen dan variabel yang berpengaruh signfikan yaitu rata-rata lama sekolah, PDRB sektor pertanian dengan interaksi dummy, PDRB sektor perdagangan dengan interaksi dummy, rata-rata lama sekolah dengan interaksi dummy, dan variabel dummy COVID-19 itu sendiri terhadap TPT.
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The Open Unemployment Rate (OUR) is an indicator used to assess social welfare and monitor changes in unemployment across regions. From 2017 to 2024, the TPT in North Sulawesi exhibited a fluctuating trend, peaking in 2020 at 7.37 percent due to the impact of COVID-19. This was followed by a gradual decline through 2024. This study utilizes four factors as independent variables: Gross Regional Domestic Product (GRDP) in the agricultural sector, GRDP in the trade sector, Labor Force Participation Rate (LFPR), and average years of schooling. The research employs panel data regression to understand the influence across both regions (districts/cities) and time periods on the OUR. The resulting model incorporates a COVID-19 interaction dummy variable. The Fixed Effect Model (FEM) was found to be the best-fitting model for analyzing OUR, with an R² of 91 percent, where average years of schooling had a significant influence on TPT. The variables that have a significant effect include the average years of schooling, agricultural GRDP with COVID-19 interaction, trade sector GRDP with COVID-19 interaction, average years of schooling with interaction, and the COVID-19 dummy variable itself.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Tingkat Pengangguran Terbuka, Regresi Data Panel, Fixed Effect Model, COVID-19, Open Unemployment Rate, Panel Data Regression, Fixed Effect Model, COVID-19
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. H Social Sciences > HA Statistics > HA31.7 Estimation Q Science Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Elizabeth Sondang I. M. S
Date Deposited:	01 Aug 2025 07:14
Last Modified:	01 Aug 2025 07:14
URI:	http://repository.its.ac.id/id/eprint/125519

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