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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Sustainable Human Capital Development Journal
Bondan Ari Wijaya
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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ENHANCING JOB FIT PREDICTION IN CORPORATIONS – A COMPARATIVE MACHINE LEARNING STUDY UTILIZING GRADIENT BOOSTING Bondan Ari Wijaya; Imam Yuadi
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 4 (2026): MARCH
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20035466

Abstract

The test results demonstrated how well the Gradient Boosting model could predict outcomes, with the model achieving the best performance metrics, such as an overall accuracy of 98% with 10-fold cross-validation. using group learning techniques to evaluate job fit. This remarkable performance was attained despite the organizational dataset's inherent class imbalance. Crucially, the model showed constant effectiveness in every aspect of job fit. The majority class, Perfect Match (98.8%), is divided into groups based on the difference between PeG and PoG. The minor groups, Overqualified (96.2%) and Underqualified (96.5%), are also divided into groups with strong accuracy and memory. "Jenjang - Main Grp "Text" and "PeG" are the two most important things that can tell you work fit," according to the feature importance analysis. These data give us a solid, objective basis for future talent management and placement decisions by clearly demonstrating that there are distinct, data-driven patterns in placing people in jobs at a company. Machine Learning, Job Fit, Human Resources, Gradient Boosting and Personnel Analytics.
The Challenge of Ideological Neutrality in Digital Content: An Examination of the Perception of Human Capital Managers in a Corporate Setting Bondan Ari Wijaya
Sustainable Human Capital Development Journal Volume 2 Issue 1
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/schade.v2i1.48796

Abstract

The rise of social media has blurred the boundaries between personal and professional lives, particularly for managers in large organizations. This study aims to explore how social media posts by Human Capital (HC) managers affect employee perceptions of their image, credibility, and neutrality. The primary research objectives are to: (1) examine the influence of social media posts on employee trust, (2) identify factors that improve or harm managerial credibility, and (3) develop strategies for managing digital reputations. The study adopts Goffman’s Impression Management Theory and Social Identity Theory to analyze the relationship between digital behavior and professional image. A descriptive qualitative methodology was used, incorporating in-depth interviews, case studies of sensitive social media posts, and document analysis of corporate policies. Findings indicate that posts related to luxury lifestyles or political opinions can polarize employee perceptions, while posts focused on professional development reinforce a positive image. This research emphasizes the importance of ethical digital communication and offers practical implications for managing HC managers' online personas to build sustainable trust within organizations
Co-Authors Imam Yuadi