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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Intan Susilawati
Universitas Riau Kepulauan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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PREDICTIVE ANALYTICS FOR EMPLOYEE TURNOVER: A COMPARATIVE STUDY BETWEEN INDUSTRIES Intan Susilawati; Oktavianti; Rizki Eka Putra
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 6 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i6.4454

Abstract

Employee turnover poses a significant challenge across industries, yet its drivers are often assumed to be universal. This study challenges that assumption through a comparative analysis of predictive analytics in the technology, healthcare, and manufacturing sectors. Utilizing human resources data and machine learning models, we identified profoundly industry-specific predictors and model performances. Results revealed distinct turnover dynamics: career-centric in technology, well-being-driven in healthcare, and structurally transactional in manufacturing. Consequently, no single predictive algorithm was universally superior. The discussion concludes that effective turnover prediction and mitigation require tailored, context-aware models aligned with the unique operational and psychological realities of each industry, rendering one-size-fits-all HR strategies obsolete and advocating for a decentralized analytical approach.
Co-Authors Oktavianti Rizki Eka Putra