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

• 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; https://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

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.