Enhancing the Accuracy of Competency Portfolio Assesments using Machine Learning: a Comparative Analysis of Predictive Models - Aditya Cahya Saputra; Imam Yuadi (Universitas Wahid Hasyim, 2026)
This study elaborates the application of various machine learning (ML) models to measure competency portfolio assessments for job grade conversion needed of employees. The purpose is choose the best ML models to enhance the accuracy, scalability, and fairness. Logistic regression and support vector machines is two traditional methods were evaluated together with random forest and gradient boosting as ensemble models and neural network as deep learning models. This study taken data of 117 employees invited to join on the competency portfolio assessment event on November 2024, all models were measured through cross-validation on parameters such as accuracy, precision and recall by Orange Data Mining. The best performance model in this study is Random Forest, achieving the highest score on Precision and Recall parameters. While Neural Networks demonstrated potential performance that almost has the same result with logistic regression. Based on this research, Random Forest can be prioritized and implemented to help the company to enhance the accuracy of competency portfolio results that needed to develop employees career, eligible competencies, and help decision making of job grade conversion assessment. Keywords: Comparative Analysis, Competency Portfolio Assessment, Machine Learning