BUS TRANSPORTATION MANAGEMENT SYSTEM: HUMAN RESOURCES II COMPETENCY, LEARNING, AND TRAINING MANAGEMENT WITH AUTOMATED FEEDBACK ANALYSIS USING NATURAL LANGUAGE PROCESSING

Abstract

As the transportation industry expands, there is a growing demand for solutions that improve operational efficiency, reliability, and adaptability in a dynamic market. This study investigates the integration of Natural Language Processing (NLP) into the HR II module, focusing on competency assessment, learning, and training management. Traditional approaches often face challenges in analyzing vast amounts of qualitative data derived from training sessions and performance evaluations. To overcome these limitations, this research proposes an automated NLP-driven method to extract actionable insights from textual data, enabling more accurate, efficient, and data-informed decision-making in employee development and performance management. This research adopts a mixed-methods approach, combining quantitative analysis of HR metrics with qualitative insights extracted through automated Natural Language Processing (NLP) techniques applied to textual feedback. Textual data are gathered from multiple HR processes, including open-ended responses from post-training surveys and performance review comments provided by supervisors and employees. Sentiment analysis is conducted to determine the emotional tone; positive, negative, or neutral, expressed in feedback related to employee performance, competencies, and training effectiveness. Additionally, topic modeling is utilized to identify key themes and topics embedded within the qualitative data. A subset of the collected data serves as training and validation sets for the selected NLP models, with evaluation metrics tailored to the specific analytical tasks to ensure model effectiveness and accuracy. The deployment of an automated feedback analysis system powered by Natural Language Processing (NLP) within the HR II competency, learning, and training management framework is projected to significantly enhance operational efficiency and effectiveness. Beyond measurable quantitative improvements, the system will provide profound qualitative insights by interpreting complex employee feedback, thereby empowering HR professionals to formulate more precise, data-driven strategies that drive meaningful improvements in workforce development and organizational performance. The integration of automated feedback analysis powered by Natural Language Processing (NLP) offers a profound opportunity to revolutionize HR II competency, learning, and training management. While challenges such as data quality, contextual nuances, and system implementation demand careful mitigation, the potential to significantly enhance operational efficiency, extract deeper actionable insights, and foster more informed, data-driven decision-making is substantial. Realizing these benefits requires a deliberate and strategic approach that seamlessly blends cutting-edge technological innovation with the critical judgment and expertise of HR professionals, ensuring the solution is both effective and contextually relevant.