TY - JOUR
T1 - Quadratic vector support machine algorithm, applied to prediction of university student satisfaction
AU - Chamorro-Atalaya, Omar
AU - Morales-Romero, Guillermo
AU - Meza-Chaupis, Yeferzon
AU - Auqui-Ramos, Elizabeth
AU - Ramos-Cruz, Jesús
AU - León-Velarde, César
AU - Aybar-Bellido, Irma
N1 - Publisher Copyright:
© 2022 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2022/7
Y1 - 2022/7
N2 - This study aims to identify the most optimal supervised learning algorithm to be applied to the prediction of satisfaction of university students. In this study, the IBM SPSS-25.0 software was used to test the reliability of the satisfaction questionnaire and the MATLAB R2021b software through the classification learner technique to determine the supervised learning algorithm. The experimental results determine a Cronbach's Alpha reliability of 0.979, in terms of the classification algorithm, it is validated that the quadratic vector support machine (SVM) has better performance metrics, being correct in 97.8% (accuracy) in the predictions of satisfaction of university students, with a recall (sensitivity) of 96.5% and an F1 score of 0.968. Likewise, when evaluating the classification model by means of the receiver operating characteristic curve (ROC) technique, it is identified that for the three expected classes of satisfaction the value of the area under the curve (AUC) is equal to 1, in such sense the predictive model through the SVM Quadratic algorithm, has a high capacity to distinguish between the 3 classes; i) dissatisfied, ii) satisfied and iii) very satisfied of satisfaction of university students.
AB - This study aims to identify the most optimal supervised learning algorithm to be applied to the prediction of satisfaction of university students. In this study, the IBM SPSS-25.0 software was used to test the reliability of the satisfaction questionnaire and the MATLAB R2021b software through the classification learner technique to determine the supervised learning algorithm. The experimental results determine a Cronbach's Alpha reliability of 0.979, in terms of the classification algorithm, it is validated that the quadratic vector support machine (SVM) has better performance metrics, being correct in 97.8% (accuracy) in the predictions of satisfaction of university students, with a recall (sensitivity) of 96.5% and an F1 score of 0.968. Likewise, when evaluating the classification model by means of the receiver operating characteristic curve (ROC) technique, it is identified that for the three expected classes of satisfaction the value of the area under the curve (AUC) is equal to 1, in such sense the predictive model through the SVM Quadratic algorithm, has a high capacity to distinguish between the 3 classes; i) dissatisfied, ii) satisfied and iii) very satisfied of satisfaction of university students.
KW - Algorithm
KW - Prediction
KW - Satisfaction
KW - University students
KW - Vector support machine
UR - http://www.scopus.com/inward/record.url?scp=85132438385&partnerID=8YFLogxK
U2 - 10.11591/ijeecs.v27.i1.pp139-148
DO - 10.11591/ijeecs.v27.i1.pp139-148
M3 - Artículo
AN - SCOPUS:85132438385
SN - 2502-4752
VL - 27
SP - 139
EP - 148
JO - Indonesian Journal of Electrical Engineering and Computer Science
JF - Indonesian Journal of Electrical Engineering and Computer Science
IS - 1
ER -