Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)

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Comparison between binomial GLMM and binomial GMET for temporary unemployment in West Java, Indonesia

Authors
Sukarna1, 2, *, Khairil Anwar Notodiputro2, Bagus Sartono2
1Universitas Negeri Makassar, Makassar, Indonesia
2IPB University, Dramaga, Indonesia
*Corresponding author.
Corresponding Author
Sukarna
Available Online 18 December 2023.
DOI
10.2991/978-94-6463-332-0_22How to use a DOI?
Keywords
Binomial GLMM; Binomial GMET; Temporary Unemployment; West Java
Abstract

Unemployment poses a significant challenge to national development. In response to this concern, the government initiated a unique survey on unemployment known as the Survey Angkatan Kerja Nasional (SAKerNas, National Labor Force Survey) in August 2022. This study aims to assess the predictive performance of two models: the binomial Generalized Linear Mixed Model (GLMM) and the binomial Generalized Mixed Effect Tree (GMET) in the context of temporary unemployment. West Java Province was selected as the focal point of this study due to its high unemployment rates in February 2022 and February 2023. The dataset for this study is based on the 2022 SAKerNas results for West Java Province, encompassing 55,957 individuals. The analysis focuses on six independent variables: age, number of household members, gender, marital status, training attendance (whether individuals have attended a course), and the highest level of education achieved. The performance of these models is evaluated using five measures: Accuracy, Sensitivity, Precision, Recall, and the F1 score. The study’s findings indicate that the binomial GMET model outperforms the binomial GLMM model in predicting temporary unemployment. However, the time required to run the R syntax of the GMET model is longer than the GLMM model.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
Series
Advances in Computer Science Research
Publication Date
18 December 2023
ISBN
978-94-6463-332-0
ISSN
2352-538X
DOI
10.2991/978-94-6463-332-0_22How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sukarna
AU  - Khairil Anwar Notodiputro
AU  - Bagus Sartono
PY  - 2023
DA  - 2023/12/18
TI  - Comparison between binomial GLMM and binomial GMET for temporary unemployment in West Java, Indonesia
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 198
EP  - 209
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_22
DO  - 10.2991/978-94-6463-332-0_22
ID  - 2023
ER  -