DOI: https://doi.org/10.14710/j.gauss.15.2.332-343

PERBANDINGAN METODE QUANTILE REGRESSION DAN BAYESSIAN QUANTILE REGRESSION (BQr) PADA DATA KRIMINALITAS DI JAWA TIMUR

*Alfisyahrina Hapsery orcid scopus  -  Universitas pgri adi buana, Surabaya, Indonesia
Ade Novadio  -  Universitas PGRI Adi Buana Surabaya, Indonesia
Artanti Indrasetianingsih  -  Universitas PGRI Adi Buana Surabaya, Indonesia
Fenny Fitriani  -  Universitas PGRI Adi Buana Surabaya, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
According to 2023 Criminal Statistics, the number of crime cases in Indonesia in 2022 will reach 372,965 cases. There was a quite drastic increase in the number of crimes in 2022. The highest crime cases based on the 2023 Crime Statistics occurred in East Java, reaching 51,905 incidents in 2022. These incidents increased drastically compared to 2021, namely 19,257 incidents. There are several factors that can influence the crime rate. To determine the factors that can influence the crime rate, it is necessary to carry out an analysis using a method that can use data that contains outliers or inhomogeneous errors. The quantile regression method is a method used to estimate parameters, where this method is used on data that is not easily affected by outliers so that it does not disturb the stability of the data. Apart from using quantile regression, this research also uses the Bayessian Quantile Regression (BQr) method. By using residual values as quantile regression method analysis. The variables used in this research are crime data, percentage of population, Open Unemployment Rate, percentage of poor population, GRDP growth rate, and Labor Force Participation Rate, Human Development Index (HDI). Each data covers districts/cities in East Java in 2024.
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Keywords: Analysis Regression; Bayessian Quantile Regression; Criminality; Quantile Regression, Sparcity

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