Analyzing Key Factors Influencing Employee Resignation Through
Decision Tree Modeling and Class Balancing Techniques
Dublin Core
Title
Analyzing Key Factors Influencing Employee Resignation Through
Decision Tree Modeling and Class Balancing Techniques
Decision Tree Modeling and Class Balancing Techniques
Subject
Employee Resignation, Decision Tree, SMOTE, HR Analytics, Feature Importance, Predictive Modeling
Description
mployee resignation poses a significant challenge to organizational stability and workforce planning. This study aims to analyze the key factors
influencing employee resignation by developing an interpretable predictive model using the Decision Tree algorithm. The analysis is conducted
on the IBM HR Analytics dataset, which includes 1,470 employee records with diverse demographic, behavioral, and job-related attributes. To
address the issue of class imbalance—where resignation cases are underrepresented—the Synthetic Minority Over-sampling Technique
(SMOTE) is applied to enhance model sensitivity and balance. After a comprehensive data preprocessing phase, including feature selection and
label encoding, the Decision Tree model is trained with a limited depth to reduce overfitting and maintain interpretability. The model achieves
an accuracy of 77%, with a recall of 0.80 and an F1-score of 0.77 for the resignation class. Feature importance analysis identifies stock option
level, job satisfaction, monthly income, relationship satisfaction, and job involvement as the most influential predictors. These findings provide
actionable insights for human resource practitioners seeking to implement targeted and data-driven employee retention strategies. The study
highlights the practical value of interpretable machine learning models in human capital analytics.
influencing employee resignation by developing an interpretable predictive model using the Decision Tree algorithm. The analysis is conducted
on the IBM HR Analytics dataset, which includes 1,470 employee records with diverse demographic, behavioral, and job-related attributes. To
address the issue of class imbalance—where resignation cases are underrepresented—the Synthetic Minority Over-sampling Technique
(SMOTE) is applied to enhance model sensitivity and balance. After a comprehensive data preprocessing phase, including feature selection and
label encoding, the Decision Tree model is trained with a limited depth to reduce overfitting and maintain interpretability. The model achieves
an accuracy of 77%, with a recall of 0.80 and an F1-score of 0.77 for the resignation class. Feature importance analysis identifies stock option
level, job satisfaction, monthly income, relationship satisfaction, and job involvement as the most influential predictors. These findings provide
actionable insights for human resource practitioners seeking to implement targeted and data-driven employee retention strategies. The study
highlights the practical value of interpretable machine learning models in human capital analytics.
Creator
Jeffri Prayitno Bangkit Saputra1,*
, Muhammad Taufik Hidayat2
, Muhammad Taufik Hidayat2
Source
https://ijiis.org/index.php/IJIIS/article/view/259/162
Publisher
Amikom Purwokerto University, Indonesia
Date
march 2025
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Jeffri Prayitno Bangkit Saputra1,*
, Muhammad Taufik Hidayat2
, “Analyzing Key Factors Influencing Employee Resignation Through
Decision Tree Modeling and Class Balancing Techniques,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9730.
Decision Tree Modeling and Class Balancing Techniques,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9730.