Prediction of Unemployment using Machine Learning Approach

Sen, Moupali; Basu, Shreya; Chatterjee, Arijit; Banerjee, Anwesha; Pal, Saheli; Mukhopadhyay, Pritam Kumar; Dutta, Stobak; Tarafdar, Arunabha

doi:10.1109/ocit56763.2022.00072

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…[2] utilized tree-based classification models, such as classification tree and random forest, to predict unemployment status. [3] employed logistic regression, SVM, KNN, and decision tree to predict unemployment status. Similarly, [4] utilized naïve bayes, logistic regression, SVM, random forest, and decision tree to predict employment status.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors

Rohaeti,

Andriyati

2024

JUITA

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Analysing pre-COVID-19 unemployment in West Java is vital for comprehending and tackling Indonesia’s economic challenges. This significance arises not only due to the region’s high unemployment rate, but also from the need to understand unemployment patterns before COVID-19, which has become more relevant now during the country’s post-pandemic recovery phase. This study evaluates four machine learning models (Random Forest, Linear SVM, RBF SVM, and Polynomial SVM) to classify employment status using demographic and job-related variables. The objective is to find the most suitable model, particularly considering the imbalanced nature of the study-case data. Data from the National Labor Force Survey (SAKERNAS) in August 2019 is utilized, comprising 54,429 respondents across districts in West Java. The four models are evaluated using holdout validation with a 70:30 stratified proportion, repeated for 100 times. Results indicate that the random forest model outperforms others in balanced accuracy, F1-score, and computational time. The random forest model also underscores the importance of gender and age in classifying employment status in West Java, suggesting a need for targeted intervention, especially for female citizens and individuals in productive age groups.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors

Rohaeti,

Andriyati

2024

JUITA

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“…Unemployment also has social implications, affecting the well-being of individuals and society as a whole [5]. The increasing rates of unemployment in Nigeria are alarming, necessitating urgent intervention from all stakeholders in the economy [6]. Unemployment is not an easy matter for most unemployed people to secure a job, and there is a disturbing growth in long-term unemployment [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Economic, Education, and Skill Factors against High Unemployment: A Case Study in West Java Province

Judijanto,

Sudi

2023

WSJEE

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This research investigates the intricate interplay between economic factors, education levels, workforce skills, and their impact on high unemployment rates, focusing on a case study in the West Java Province. Utilizing a quantitative approach, the study employs Structural Equation Modeling with Partial Least Squares (SEM-PLS) to analyze data collected from 187 participants. The measurement model's reliability and validity are assessed, and subsequent regression analyses explore the relationships between the identified constructs. The discriminant validity matrix ensures the distinctiveness of the constructs. Fit indices compare the Estimated Model with the Saturated Model, affirming the model's accuracy in capturing observed data. Noteworthy results highlight the significance of education and workforce skills in mitigating high unemployment. The research contributes to the existing literature by providing empirical insights into the multifaceted factors influencing unemployment in the specific context of West Java.

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Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms

Berigel,

Boztaş,

Rocca

et al. 2024

Socio-Economic Planning Sciences

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Prediction of Unemployment using Machine Learning Approach

Cited by 5 publications

References 9 publications

Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors

Comparative Study of Predictive Classification Models on Data with Severely Imbalanced Predictors

Analysis of Economic, Education, and Skill Factors against High Unemployment: A Case Study in West Java Province

Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms

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