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| Format: | Recurso digital |
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Zenodo
2025
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| Accès en ligne: | https://doi.org/10.5281/zenodo.15698241 |
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| _version_ | 1866901521009999872 |
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| author | Farras, Naufal Alif |
| author_facet | Farras, Naufal Alif |
| contents | <p>The dataset used in this study consists of job posting data collected from LinkedIn Indonesia, curated to support research on predicting job post popularity using machine learning classification models. It contains structured information from publicly available job listings, with each row representing a unique job post. Key features include job title, company name, industry, employment type, job function, job location, seniority level, and description length, along with metadata such as the number of company followers and the date the job was posted.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15698241 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predicting LinkedIn Job Post Popularity in Indonesia Using Machine Learning Classification Models DATASET Farras, Naufal Alif <p>The dataset used in this study consists of job posting data collected from LinkedIn Indonesia, curated to support research on predicting job post popularity using machine learning classification models. It contains structured information from publicly available job listings, with each row representing a unique job post. Key features include job title, company name, industry, employment type, job function, job location, seniority level, and description length, along with metadata such as the number of company followers and the date the job was posted.</p> |
| title | Predicting LinkedIn Job Post Popularity in Indonesia Using Machine Learning Classification Models DATASET |
| url | https://doi.org/10.5281/zenodo.15698241 |