Enhancing Competency Level Prediction Using Machine Learning: A Data-Driven Approach Based on Psychological Assessment Data - Sinung Suakanto; Joko Siswanto; Jan M. Pawlowski; Muharman Lubis; Syfa Nur Lathifah; Litasari Widyastuti Suwarsono (Bina Nusantara University, 2026)
Competency level prediction plays a crucial role in competency-based human resource management such as talent management. Talent management is achieved by identifying individuals’ knowledge, skills, and attitudes through psychological assessment. Recognizing employees as a strategic asset by accurately predicting competencies supports targeted development, boosting individual and organizational performance. Current practices related to competency assessment require expert judgment from psychologists or assessors, which can be time-consuming. The research proposes a machine learning–based approach to predict competency levels using psychological assessment scores as input, designed to operate within digital, network-enabled interview platforms. Several machine learning methods, including Random Forests, k-Nearest Neighbors (KNN), and Support Vector Machines (SVMs), are applied to historical assessment datasets to identify patterns and relationships between psychological assessment scores and competency levels.The dataset comprises 1,220 records from a psychological assessment. The experimental results indicate that the Random Forest model achieves the highest accuracy of 81%, outperforming other models in competency level prediction. The key novelty lies in its data-driven methodology, which enhances the objectivity and efficiency of competency evaluation while reducing reliance on expert interpretation. By enabling automated competency prediction in network-enabled interview environments, the proposed approach supports more efficient talent decision-making, workforce development, and recruitment processes. The findings demonstrate that machine learning can accurately predict competency levels from a clean dataset of psychological assessment scores, achieving accuracy above 80%. Future research may enhance model robustness by incorporating additional assessment center criteria and real-world performance metrics.