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All Journal Jurnal Review Pendidikan dan Pengajaran (JRPP) Musamus Journal Of Research Information and Communication Technology REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Sustainable: Jurnal Kajian Mutu Pendidikan Jurnal Responsif : Riset Sains dan Informatika Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ekonomi, Teknologi dan Bisnis
Rifki Dwi Kurniawan
STMIK YMI TEGAL
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Machine Learning Model for Human Resource Placement in Higher Education Syefudin Syefudin; Rifki Dwi Kurniawan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6861

Abstract

This study presents the development and evaluation of a machine learning model designed to support human resource (HR) placement decisions in higher education institutions. Using combined personnel data from STMIK YMI Tegal and Politeknik Harber, we built a predictive model to estimate staff attendance at institutional progress reporting events, a critical indicator for performance evaluation and role suitability. The dataset comprised 137 records with six categorical predictors: Position, Homebase, Origin, Tegal_Status, Gender, and Institution. Categorical variables were encoded using label encoding, and a Random Forest classifier was trained using a stratified 75%/25% train-test split. The model achieved a held-out test accuracy of 97.14%, precision of 93.33%, recall of 100%, and F1-score of 96.55%, outperforming baseline models (Logistic Regression and Decision Tree). Five-fold cross validation confirmed robust generalization with an average accuracy of 91.22%. Feature importance analysis revealed Position as the most influential variable (76.88% importance), followed by Homebase and Origin. The results suggest that machine learning, particularly ensemble based methods, can provide reliable decision support tools for HR managers in academic settings, enabling data driven placement strategies. This research highlights the potential of predictive analytics for optimizing staff assignments and fostering institutional effectiveness. Future work should include larger datasets, additional features, and external validation to enhance model generalizability.
Co-Authors Ahmad Zulfikri Ardi Supratman Bangkit Indarmawan Nugroho Chalimatus Solikhah Dwi Fina Fahirah Gunawan Gunawan Gunawan Intan Mayla Faiza Maezun Nafis Mohamad Rifki Septiadi Muhamad Ainurohman Nindy Putri Lestari Nugroho Adhi Santoso Nugroho Adhi Santoso Nurul Fadilah Sarif Surorejo Syefudin, Syefudin Zakaria