Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) Indonesian Journal of Electrical Engineering and Computer Science
Alejos-Ipanaque, Rufino
Unknown Affiliation
Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Identification of factors that influence student satisfaction from the analysis of voice messaging from WhatsApp: a case study Chamorro-Atalaya, Omar; Aquije-Cardenas, Giorgio; Carranza-Noriega, Raymundo; Moreno-Chinchay, Lilly; Medina-Bedón, Yurfa; Alejos-Ipanaque, Rufino; Tasayco-Jala, Abel; Gonzales-Saldaña, Susan
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i5.27328

Abstract

In these times when there is talk of a return to a new normality in education after what happened due to the pandemic, it is necessary to permanently evaluate the perception of student satisfaction, contributing to the results obtained through traditional methods such as the survey, with methods in which open opinions can be analyzed as in the case of voice analysis. In this sense, this article describes a case study, which aims to identify the factors that influence student satisfaction with respect to teaching performance, based on the analysis of WhatsApp voice messaging. The study has a qualitative approach, exploratory level and non-experimental design. It was possible to identify various factors grouped into five categories: i) planning; ii) didactic strategies; iii) communication; iv) administration of the class session; and v) professional and personal characteristics of the teacher. Therefore, it is concluded that it is possible to close the gaps between the factors that are sensitive and relevant for the university, when a questionnaire with delimited questions is applied to observe only some factors of student satisfaction, with respect to those sensitive factors and relevant to students, by analyzing their comments from the use of voice messaging from mobile applications.
Automatic learning algorithm for troubleshooting in hydraulic machinery Castro-Puma, Jose; Castro-Puma, Miguel; More-Sánchez, Verónica; Marcos-Romero, Juana; Huamán-Flores, Elio; Poma-Garcia, Claudia; Alejos-Ipanaque, Rufino
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 Aquije-Cardenas, Giorgio Carranza-Noriega, Raymundo Castro-Puma, Jose Castro-Puma, Miguel Chamorro-Atalaya, Omar Gonzales-Saldaña, Susan Huamán-Flores, Elio Marcos-Romero, Juana Medina-Bedón, Yurfa More-Sánchez, Verónica Moreno-Chinchay, Lilly Poma-Garcia, Claudia Tasayco-Jala, Abel