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...
Autores: | , , |
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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 |
dc.type.version.es_PE.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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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 |
dc.rights.es_PE.fl_str_mv |
info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-sa/4.0/ |
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openAccess |
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http://creativecommons.org/licenses/by-sa/4.0/ |
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application/pdf |
dc.publisher.es_PE.fl_str_mv |
Institute of Advanced Engineering and Science |
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Repositorio Institucional - UTP Universidad Tecnológica del Perú |
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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. This research recommend the implementation of this model in all SUNAT’s controls locations in Peru.Campus Lima Centroapplication/pdfengInstitute of Advanced Engineering and ScienceIDIndonesian Journal of Electrical Engineering and Computer Science;vol. 31, n° 1info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/Repositorio Institucional - UTPUniversidad Tecnológica del Perúreponame:UTP-Institucionalinstname:Universidad Tecnológica del Perúinstacron:UTPHuman talent managementMachine learningPredictive modellinghttps://purl.org/pe-repo/ocde/ford#5.02.04https://purl.org/pe-repo/ocde/ford#1.02.00Human resource optimization using linear regression machine learning model: case study SUNATinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionORIGINALC.Palomino_Articulo_2023.pdfC.Palomino_Articulo_2023.pdfapplication/pdf920061http://repositorio.utp.edu.pe/bitstream/20.500.12867/7232/1/C.Palomino_Articulo_2023.pdf83a0f2112035572dc0f98a939bef222cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.utp.edu.pe/bitstream/20.500.12867/7232/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52TEXTC.Palomino_Articulo_2023.pdf.txtC.Palomino_Articulo_2023.pdf.txtExtracted texttext/plain21381http://repositorio.utp.edu.pe/bitstream/20.500.12867/7232/3/C.Palomino_Articulo_2023.pdf.txtff91ed1a90b2b60bbbfc78e78b1a30e9MD53THUMBNAILC.Palomino_Articulo_2023.pdf.jpgC.Palomino_Articulo_2023.pdf.jpgGenerated Thumbnailimage/jpeg21215http://repositorio.utp.edu.pe/bitstream/20.500.12867/7232/4/C.Palomino_Articulo_2023.pdf.jpg68458f2f209c318fa07c43b76f064739MD5420.500.12867/7232oai:repositorio.utp.edu.pe:20.500.12867/72322023-08-01 14:04:14.801Repositorio Institucional de la Universidad Tecnológica del Perúrepositorio@utp.edu.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 |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).