Lasso Regression and Elastic Net in Analysing Factors Affecting the Open Unemployment Rate
DOI:
https://doi.org/10.30812/xey50x64Keywords:
Elastic Net, Lars Algorithm, Lasso Regression, UnemploymentAbstract
This study aims to identify and analyze the variables that affect the open unemployment rate in Banten Province, Indonesia. The analyzed variables include population density, average years of schooling, labor force participation rate, minimum wage, Provincial GRDP, total labor force, and the number of poor people. The method used in this study is multiple linear regression analysis with secondary data from the Central Bureau of Statistics (BPS) for the period 2017–2022. The analysis revealed multicollinearity in the average years of schooling variable, with a Variance Inflation Factor (VIF) >10. To address this issue, Lasso regression and Elastic Net regression were applied. The results of this study show that Lasso regression produces a model with a Mean Squared Error (MSE) of 1.3234857, while Elastic Net regression yields a model with a lower MSE of 0.180683, indicating better predictive performance. The best model for predicting the open unemployment rate in Banten Province is the Elastic Net regression. The variables that significantly affect the open unemployment rate are average years of schooling, labor force participation rate, minimum wage, Provincial GRDP, total labor force, and the number of poor people. The conclusion of this study is that Elastic Net regression is more effective in predicting the open unemployment rate than other methods. The implication of these findings is that the generated model can serve as a basis for formulating more effective labor policies to reduce the unemployment rate in Banten Province.
Downloads
References
Adriyanto, A., Prasetyo, D., & Khodijah, R. (2020). Angkatan Kerja dan Faktor yang Mempengaruhi Pengangguran. Jurnal Ilmu
Ekonomi & Sosial, 11(2), 66–82. https://doi.org/10.35724/jies.v11i2.2965
Altelbany, S. (2021). Journal of Applied Economics and Business. Journal of Applied Economics and Business, 5(1), 131–142.
https://doi.org/https://doi.org/10.34260/jaebs.517Journal
Andana, A. P., Safitri, D., & Rusgiyono, A. (2017). Model regresi menggunakan least absolute shrinkage and selection operator
(lasso) pada data banyaknya gizi buruk kabupaten/kota di Jawa Tengah. Jurnal Gaussian, 6(1), 21–30.
Astuti, N. F. V., & Setyonaluri, D. (2022). Labor Market Outcomes of Vocational High Schools (SMK) and General High Schools
(SMA) during the COVID-19 Pandemic. The Journal of Indonesia Sustainable Development Planning, 3(3), 278–293.
https://doi.org/10.46456/jisdep.v3i3.328
Fanny, R., Djuraidah, A., & Alamudi, A. (2018). Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elasticnet.
Xplore: Journal of Statistics, 2(2), 7–14. https://doi.org/10.29244/xplore.v2i2.89
Fatkhu Rokhim, Novianti, T., & Anggraeni, L. (2023). Factors Influencing Unemployment in Indonesia. Journal of Scientific
Research, Education, and Technology (JSRET), 2(1), 122–131. https://doi.org/10.58526/jsret.v2i1.51
Frans L, O., Rizki, S. W., & Kusnandar, D. (2022). Pemodelan Pertumbuhan Ekonomi Kalimantan Barat Menggunakan Pendekatan
Least Absolute Shrinkage And Selection Operator (LASSO). Bimaster: Buletin Ilmuah Math, Stat dan Terapannya,
11(1), 111–120.
Handayani, A., & Wachidah, L. (2023). Metode Regresi Elastic-net untuk Mengatasi Masalah Multikolinearitas pada Kasus Tingkat
Pengangguran Terbuka di Provinsi Jawa Barat. Bandung Conference Series: Statistics, 3(1), 66–72. https://doi.org/10.
29313/bcss.v3i1.5757
Hastie Trevor and Qian Junyang. (2014). Glmnet Vignette, 1–42.
Katemba, P., & Djoh, R. K. (2017). Prediksi Tingkat Produksi Kopi Menggunakan Regresi Linear. Jurnal Ilmiah Flash, 3(1), 42.
https://doi.org/10.32511/flash.v3i1.136
Kayanan, M., & Wijekoon, P. (2019). Performance of LASSO and Elastic net estimators in Misspecified Linear Regression Model.
Ceylon Journal of Science, 48(3), 293. https://doi.org/10.4038/cjs.v48i3.7654
Lindner, T., Puck, J., & Verbeke, A. (2020). Misconceptions about multicollinearity in international business research: Identification,
consequences, and remedies. Journal of International Business Studies, 51(3), 283–298. https : / / doi . org / 10 . 1057 /
s41267-019-00257-1
Marini, L., & Putri, N. T. (2019). Peluang Terjadinya Pengangguran di Povinsi Bengkulu : Sebererapa Besar? 1(1), 70–83.
Mubasher, S., Zakria, M., Shahzad, A., Ali, N., & Faisal, H. (2024). Dynamic Ridge Regression vs. Lasso Regression: A Comparative
Study for Modeling Pakistan’s Unemployment Rate. Global Journal of Mathematics and Statistics, (November 2024),
21–45. https://doi.org/10.61424/gjms
Nur, A. R., Jaya, A. K., & Siswanto, S. (2023). Comparative Analysis of Ridge, LASSO, and Elastic Net Regularization Approaches
in Handling Multicollinearity for Infant Mortality Data in South Sulawesi. Jurnal Matematika, Statistika dan Komputasi,
20(2), 311–319. https://doi.org/10.20956/j.v20i2.31632
Pohlan, L. (2024). Unemployment’s long shadow: the persistent impact on social exclusion. Journal for Labour Market Research,
58(1). https://doi.org/10.1186/s12651-024-00369-8
Putra, I. K. A. A., & Arka, S. (2016). Analisis Pengaruh Tingkat Pengangguran Terbuka, Kesempatan Kerja, Dan Tingkat Pendidikan
Terhadap Tingkat Kemiskinan Pada Kabupaten / Kota Di Provinsi Bali. EP Unud, 7(3), 416–444.
Ranstam, J.,&Cook, J. A. (2018). LASSO regression. British Journal of Surgery, 105(10), 1348. https://doi.org/10.1002/bjs.10895
Romhadhoni, P., Faizah, D. Z., & Afifah, N. (2019). Pengaruh Produk Domestik Regional Bruto (PDRB) Daerah terhadap Pertumbuhan
Ekonomi dan Tingkat Pengangguran Terbuka di Provinsi DKI Jakarta. Jurnal Matematika Integratif, 14(2), 113.
https://doi.org/10.24198/jmi.v14.n2.19262.113-120
Rusydan, R. M., & Wijaya, R. S. (2024). Impact of Economic Growth, Minimum Wage, Labor Force Participation Rate, and Population
Size on the Open Unemployment. Journal of Business Management and Economic Development, 2(03), 1186–
1198. https://doi.org/10.59653/jbmed.v2i03.911
Saputro, D. R. S., Wahyu, N. L., & Widyaningsih, Y. (2025). Performance of Ridge Regression, Least Absolute Shrinkage and
Selection Operator, and Elastic Net in Overcoming Multicollinearity. Journal of Multidisciplinary Applied Natural
Science, 5(2), 370–382. https://doi.org/10.47352/jmans.2774-3047.251
Septiana, D., & Hasyim, S. (2025). The Effect of Labor Force , Minimum Wage , and Per Capita Income on Unemployment Rate in
Five Southeast Asian Countries TALENTA Conference Series The Effect of Labor Force , Minimum Wage , and Per Capita
Income on Unemployment Rate in Five Southeast Asian. 8(2). https://doi.org/10.32734/lwsa.v8i1.2409
Setiawan, K., Haikal, M.,Wicaksana, A. G., & Dermawan, D. (2023). Analisis Faktor-Faktor Yang Mempengaruhi Tingkat Pengangguran
Terbuka Di Provinsi Banten 2017-2021. Jurnal Riset Rumpun Ilmu Ekonomi, 2(1), 107–120. https://doi.org/10.
55606/jurrie.v2i1.1112
Vilanasari, V. (2023). Penerapan Rgresi Elastic Net Pada Faktor Yang Mempengaruhi Indeks Ketimpangan Gender Di
Indonesia [Doctoral dissertation]. Universitas Negeri Malang.
Waldmann, P., M´esz´aros, G., Gredler, B., Fuerst, C., & S¨olkner, J. (2013). Evaluation of the lasso and the elastic net in genome-wide
association studies. Frontiers in Genetics, 4(DEC), 1–11. https://doi.org/10.3389/fgene.2013.00270
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Anita Mustikasari, Andi Daniah Pahrany
This work is licensed under a Creative Commons Attribution 4.0 International License.
