K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment

• Autores: Palacios Huaraca, Carlos Rubén, Chamorro Atalaya, Omar Freddy, Morales-Romero, Guillermo, Quispe-Andía, Adrián, Caycho-Salas, Beatriz, Auqui-Ramos, Elizabeth Katerin, Ramos-Salazar, Primitiva
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/5585
• Enlace del recurso: https://hdl.handle.net/20.500.12867/5585; https://doi.org/10.37135/chk.002.14.06
• Nivel de acceso: acceso abierto
• Materia: Predictive analytics; Quality of service; Supervised learning; https://purl.org/pe-repo/ocde/ford#2.02.03

The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.