Human resource optimization using linear regression machine learning model: case study SUNAT

Descripción del Articulo

The continue searching for organization’s process improvement for reduce cost and increase efficiency is a big challenge for organizations nowadays. This paper is about to recognize the importance of process improvement focusing in the right human resource allocation. The research predict best optim...

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Detalles Bibliográficos
Autores: Palomino Vidal, Carlos Efraín, Salazar Marín, Gloria, Condori Obregon, Patricia
Formato: artículo
Fecha de Publicación:2023
Institución:Universidad Tecnológica del Perú
Repositorio:UTP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.utp.edu.pe:20.500.12867/7232
Enlace del recurso:https://hdl.handle.net/20.500.12867/7232
http://doi.org/ 10.11591/ijeecs.v31.i1.pp386-391
Nivel de acceso:acceso abierto
Materia:Human talent management
Machine learning
Predictive modelling
https://purl.org/pe-repo/ocde/ford#5.02.04
https://purl.org/pe-repo/ocde/ford#1.02.00
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dc.title.es_PE.fl_str_mv Human resource optimization using linear regression machine learning model: case study SUNAT
title Human resource optimization using linear regression machine learning model: case study SUNAT
spellingShingle Human resource optimization using linear regression machine learning model: case study SUNAT
Palomino Vidal, Carlos Efraín
Human talent management
Machine learning
Predictive modelling
https://purl.org/pe-repo/ocde/ford#5.02.04
https://purl.org/pe-repo/ocde/ford#1.02.00
title_short Human resource optimization using linear regression machine learning model: case study SUNAT
title_full Human resource optimization using linear regression machine learning model: case study SUNAT
title_fullStr Human resource optimization using linear regression machine learning model: case study SUNAT
title_full_unstemmed Human resource optimization using linear regression machine learning model: case study SUNAT
title_sort Human resource optimization using linear regression machine learning model: case study SUNAT
author Palomino Vidal, Carlos Efraín
author_facet Palomino Vidal, Carlos Efraín
Salazar Marín, Gloria
Condori Obregon, Patricia
author_role author
author2 Salazar Marín, Gloria
Condori Obregon, Patricia
author2_role author
author
dc.contributor.author.fl_str_mv Palomino Vidal, Carlos Efraín
Salazar Marín, Gloria
Condori Obregon, Patricia
dc.subject.es_PE.fl_str_mv Human talent management
Machine learning
Predictive modelling
topic Human talent management
Machine learning
Predictive modelling
https://purl.org/pe-repo/ocde/ford#5.02.04
https://purl.org/pe-repo/ocde/ford#1.02.00
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.02.04
https://purl.org/pe-repo/ocde/ford#1.02.00
description The continue searching for organization’s process improvement for reduce cost and increase efficiency is a big challenge for organizations nowadays. This paper is about to recognize the importance of process improvement focusing in the right human resource allocation. The research predict best optime human resource allocation in the Superintendencia Nacional de Aduanas (SUNAT) in the chemical materials control area using a linear regression machine learning algorithm. This model was validated with recollected data in the SUNAT’s control locations, the results were compared with historical data to determine their efficiency obtained a mean square error 0.434 that is lower comparing to logistic regression and support vector machine algorithm. This research recommend the implementation of this model in all SUNAT’s controls locations in Peru.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-01T17:39:39Z
dc.date.available.none.fl_str_mv 2023-08-01T17:39:39Z
dc.date.issued.fl_str_mv 2023
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.none.fl_str_mv 2502-4760
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12867/7232
dc.identifier.journal.es_PE.fl_str_mv Indonesian Journal of Electrical Engineering and Computer Science
dc.identifier.doi.none.fl_str_mv http://doi.org/ 10.11591/ijeecs.v31.i1.pp386-391
identifier_str_mv 2502-4760
Indonesian Journal of Electrical Engineering and Computer Science
url https://hdl.handle.net/20.500.12867/7232
http://doi.org/ 10.11591/ijeecs.v31.i1.pp386-391
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.ispartofseries.none.fl_str_mv Indonesian Journal of Electrical Engineering and Computer Science;vol. 31, n° 1
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dc.publisher.es_PE.fl_str_mv Institute of Advanced Engineering and Science
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dc.source.es_PE.fl_str_mv Repositorio Institucional - UTP
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spelling Palomino Vidal, Carlos EfraínSalazar Marín, GloriaCondori Obregon, Patricia2023-08-01T17:39:39Z2023-08-01T17:39:39Z20232502-4760https://hdl.handle.net/20.500.12867/7232Indonesian Journal of Electrical Engineering and Computer Sciencehttp://doi.org/ 10.11591/ijeecs.v31.i1.pp386-391The continue searching for organization’s process improvement for reduce cost and increase efficiency is a big challenge for organizations nowadays. This paper is about to recognize the importance of process improvement focusing in the right human resource allocation. The research predict best optime human resource allocation in the Superintendencia Nacional de Aduanas (SUNAT) in the chemical materials control area using a linear regression machine learning algorithm. This model was validated with recollected data in the SUNAT’s control locations, the results were compared with historical data to determine their efficiency obtained a mean square error 0.434 that is lower comparing to logistic regression and support vector machine algorithm. 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