Identifying Workforce Size Determinants in Rebana and Arumanis Using Least Absolute Shrinkage and Selection Operator (LASSO) Regression

Ika Nur Laily Fitriana; Fonda Leviany; Kurnia Sari Kasmiarno; Nuramaliyah

doi:10.36456/jstat.vol18.no2.a10790

Authors

Ika Nur Laily Fitriana Universitas Terbuka
Fonda Leviany Universitas Terbuka
Kurnia Sari Kasmiarno Universitas Terbuka
Nuramaliyah Universitas Terbuka

DOI:

https://doi.org/10.36456/jstat.vol18.no2.a10790

Keywords:

Arumanis Area, Rebana Area, Variable Selection, Workforce size, LASSO Regression

Abstract

Development of West Java’s Rebana and Arumanis Areas is intended to strengthen the economy and expand employment opportunities. Identifying influential factors is essential for formulating effective and well-targeted policies to enhance workforce absorption in these two areas. This study aims to analyze and identify the main factors affecting workforce size in the Rebana and Arumanis Areas using the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. To determine the optimal (L1) penalty in LASSO, 5-fold cross-validation (k = 5) was applied, yielding an optimal penalty value of 363.03. The results indicate that the factors ranked from most to least important for workforce absorption in Rebana and Arumanis are: (1) the number of MSMEs, (2) the Human Development Index (HDI), (3) Gross Regional Domestic Product (GRDP), (4) the regional minimum wage, (5) realized Foreign Direct Investment (FDI), (6) road length, and (7) realized Domestic Direct Investment (DDI). Model performance was evaluated using an average (R²) of 83.09%. These findings highlight the importance of strengthening the MSME ecosystem and implementing productivity-oriented regional development planning to promote workforce absorption in Rebana and Arumanis.

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