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

Year: 2023

Type: book-chapter

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.

Source: Advances in computer science research

DOI: 10.2991/978-94-6463-332-0_22

Authors: Sukarna, Khairil Anwar Notodiputro, Bagus Sartono

Institutions: IPB University, State University of Makassar

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Funder: Institut Pertanian Bogor