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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Rufino Alejos-Ipanaque
Universidad Nacional del Callao

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Supervised learning using support vector machine applied to sentiment analysis of teacher performance satisfaction Omar Chamorro-Atalaya; Dora Arce-Santillan; José Antonio Arévalo-Tuesta; Lilia Rodas-Camacho; Ronald Fernando Dávila-Laguna; Rufino Alejos-Ipanaque; Lilly Rocío Moreno-Chinchay
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp516-524

Abstract

Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.
Automatic learning algorithm for troubleshooting in hydraulic machinery Jose Castro-Puma; Miguel Castro-Puma; Verónica More-Sánchez; Juana Marcos-Romero; Elio Huamán-Flores; Claudia Poma-Garcia; Rufino Alejos-Ipanaque
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp535-544

Abstract

In Peru, there are many companies linked to the category of heavy machinery maintenance, in which, on the one hand, although it is true they generate a record of events linked to equipment maintenance indicators, on the other hand they do not make efficient use of these data generating operational patterns, through machine learning, that contribute to the improvement of processes linked to the service. In this sense, the objective of this article is to generate a tool based on automatic learning algorithms that allows predicting the location of faults in hydraulic excavators, in order to improve the management of the maintenance service. When developing the research, it was obtained that the algorithm that assembles bagged trees presents an accuracy of 97.15%, showing a level of specificity of 99.04%, an accuracy of 98.56% and a sensitivity of 97.12%. Therefore, the predictive model using the ensemble bagged trees algorithm shows significant performance in locating the system where failures occur in hydraulic excavator fleets. It is concluded then that it was possible to improve aspects associated with the planning and availability of supplies or components of the maintenance service, also optimizing the continuity and response capacity in the maintenance process.
Co-Authors Claudia Poma-Garcia Dora Arce-Santillan Elio Huamán-Flores José Antonio Arévalo-Tuesta Jose Castro-Puma Juana Marcos-Romero Lilia Rodas-Camacho Lilly Rocío Moreno-Chinchay Miguel Castro-Puma Omar Chamorro-Atalaya Ronald Fernando Dávila-Laguna Verónica More-Sánchez