Vector support machine algorithm applied to the improvement of satisfaction levels in the acquisition of professional skills

• Autores: León Velarde, César Gerardo, Chamorro-Atalaya, Omar, Ortega-Galicio, Orlando, Morales-Romero, Guillermo, Villar-Valenzuela, Darío, Meza-Chaupis, Yeferzon, Quevedo-Sánchez, Lourdes
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/5813
• Enlace del recurso: https://hdl.handle.net/20.500.12867/5813; http://doi.org/10.11591/ijeecs.v26.i1.pp597-604
• Nivel de acceso: acceso abierto
• Materia: Learning algorithm; Virtual learning; Satisfaction; https://purl.org/pe-repo/ocde/ford#2.02.03

The study carried out identifies the metricss of the predictive model obtained through the support vector machine (VSM) algorithm, which will be applied in the satisfaction of the acquisition of professional skills of the students of the professional engineering career. As part of the development, the statistical classification tool is used, during the development of the research, it was identified that the predictive model presents as general metrics an accuracy of 82.1%, a precision of 70.72%, a sensitivity of 91.06% and a specificity of 87.60%. Through this model, it contributes significantly to decision-making in relation to improving satisfaction related to the acquisition of professional skills in engineering students, since decision-making by university authorities will have a scientific basis, to take early and timely actions in relation to the predictive elements.