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

Aug 5, 2026

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Continuous process improvement to reduce cost and increase efficiency remains a major challenge for organizations. The study focuses on selecting the right human resource allocation in SUNAT’s chemical materials control area by predicting optimal staffing using a linear regression machine learning approach. The model is validated with recollected intervention data and compared against historical results and alternative methods. The linear regression model achieves a mean squared error of 0.434, lower than logistic regression and support vector machine, and is recommended for deployment across SUNAT control locations in Peru.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction

Problem in SUNAT chemical materials control

Proposed solution with machine learning

Data collection and modeling approach

Method for forecasting and evaluation

Indonesian Journal of Electrical Engineering and Computer Science Vol. 31, No. 1, July 2023, pp. 386∼391 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v31.i1.pp386-391 ❒ 386 | Human resource optimization using linear regression machine learning model: case study SUNAT
Salazar Marn Gloria1 , Condori Obregon Patricia1,2 , Palomino Vidal Carlos3
1Image Processing Research Laboratory (INTI-Lab) Universidad de Ciencias y Humanidades, Lima, Per´u
2Business on Engineering and Technology, S.A.C. (BE Tech), Lima, Per´u
3Facultad de Ingenier´ıa, Universidad Tecnolgica del Per´u, Lima, Per´u | | | | --- | --- | --- | | Article Info
Article history:
Received Nov 3, 2022 Revised Mar 10, 2023 Accepted Mar 12, 2023
Keywords:
Human resource allocation Linear regression Logistic regression Machine learning Support vector machine | | ABSTRACT
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 Per´u .
This is an open access article under the CC BY-SA license. | | Corresponding Author: | | | | Palomino Vidal Carlos
Facultad de Ingenier´ıa, Universidad Tecnolgica del Per´u Av. Petit Thouars 116, Lima 15046, Per´u
Email: [carlospalomino@outlook.com](carlospalomino@outlook.com) | | | 1. INTRODUCTION Business organization has a continuos effort in improve their processes for reduce cost and resources to increase their revenue. One of the most important factors is optimize the human resource and just have the right quantity of workers in each processes or activity. This study is focused in determine the optimal human resource allocation in the chemical materials control area in Superintendencia Nacional de Aduanas (SUNAT) . In the literature review is available many papers related to the importance of human resource allocation, many factors are consider for these aim like, quantity of workers or find a mix of skills that cover the job and the process’s requirements [1]-[5] . Also in this literature shows the most using method to choose the optimal resource allocation are statistical methods and machine learning (ML) algorithm [6]-[10] . Some of the most algorithm used to predict human resource allocation are neural network algorithm used for predict human resource allocation for container terminal [11]-[13] . Other algorithm used are the decision tree and linear regression both combined in a multilayer perception algorithm were used for predict human resource allocation for different business processes [14] . The resource allocation permits organizations achieve organizational goals to improve cost, time or quality [7] . One of the most important problems in the chemical materials control area in SUNAT is determine the right quantity of workers required for the three activities: visual inspection, document inspection and inside inspection. This activities are needed to determine if the vehicles bring some of the prohibited materials inside, this prohibited materials are use for drug elaboration, when the traffic of vehicles is high a large row of cars are accumulate, cause discomfort among drivers in this case the workers are not enough and sometimes let the vehicles pass without the correct supervision, but some days there

FAQs

What human resource problem does the study address in SUNAT?

It targets determining the appropriate number of workers for three chemical materials control activities: visual inspection, document inspection, and inside inspection.

Which machine learning method is proposed to optimize staffing?

The study proposes using a linear regression machine learning algorithm to predict the number of workers needed for each weekday based on historical intervention data.

How is the model evaluated and how does it compare to other algorithms?

Data are split into 70% training and 30% testing, with performance measured using metrics such as R-squared and mean squared error. The linear regression model’s mean squared error is 0.434, lower than logistic regression and support vector machine results.

Aug 5, 2026

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