Personalized Learning Models Using Decision Tree and Random Forest Algorithms in Telecommunication Company
DOI: http://dx.doi.org/10.62527/joiv.8.1.1905
Abstract
In response to the rising popularity of online training, this study addresses the crucial need for effective assessment methods at PT XYZ. The research focuses on developing a comprehensive solution through a data visualization dashboard and a machine learning model. The data visualization dashboard, created using Tableau, provides an interactive platform for exploring training data. It offers valuable insights into employees learning progress and needs, empowering them to monitor their advancement and identify areas for improvement effectively. Simultaneously, a machine learning model was developed using Python and Google Collab, employing decision trees and random forest algorithms. The model exhibited promising results with an accuracy rate of 69% for decision trees and 70% for random forests, indicating its proficiency in predicting skill groups. Furthermore, the study rigorously evaluated the dashboard and machine learning model using a 20% holdout dataset, affirming their effectiveness. The dashboard, deployed on a web server, ensures accessibility to all PT XYZ employees, enhancing user experience and engagement. Notably, the dashboard's user-friendly interface allows employees to actively participate in their learning journey, while the machine learning model generates personalized training recommendations based on their progress and needs. In summary, this research provides a practical and innovative solution to the challenge of online training assessment at PT XYZ. By combining data visualization techniques and machine learning algorithms, the developed tools significantly enhance the efficiency and effectiveness of training programs. These findings contribute valuable insights into online training assessment methodologies and pave the way for improved learning experiences in the digital age.
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