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All Journal Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA)
Alifya Salsabilla
Universitas Mataram
Social Sciences

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Determinan Pengangguran Terdidik Tahun 2023 Menggunakan Analisis Regresi Logistik Biner Alifya Salsabilla; Luluk Fadliyanti; Vici Handalusiana Husni
Kaganga:Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora Vol. 8 No. 1 (2025): Kaganga: Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/kaganga.v8i1.13760

Abstract

This study aims to determine the effect of gender, age, marital status, job training, work experience and area of ​​residence on educated unemployment in West Nusa Tenggara Province (NTB) in 2023 using binary logistic regression. The method used is binary logistic regression analysis using the Stata 17 application and using data from the National Labor Force Survey (SAKERNAS) in August 2023 with a sample size of 5,807. The results of this study indicate that factors such as gender, age, marital status, job training, work experience, and area of ​​residence have a significant role in determining a person's chances of becoming educated unemployed. This study concludes that female gender, young individuals, individuals with divorced status, individuals who have not received job training, and area of ​​residence have a higher chance of becoming educated unemployed. Meanwhile, marital status that is married, divorced, and individuals with work experience have a smaller chance of becoming educated unemployed. Keywords: Binary Logistic Regression Analysis, Determinants of Educated Unemployment.
Co-Authors Fadliyanti, Luluk Vici Handalusiana Husni