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Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM

Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM untuk Mendapatkan Estimasi Robust Regression Terbaik dalam Perkara Pidana di Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM

Jurnal Matematika, Statistika dan Komputasi · Vol. 18 No. 2 (2022) · pp. 251–260

DOI
10.20956/j.v18i2.18630
ISSN
2614-8811, 1858-1382
Published
2022-01-01
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98

Keywords

Collaboration network of the authors

16 authors · 19 co-authorship links

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  • Islamic University of Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Nahdah Ghina HandayaniIslamic University of Indonesia1
Nur FaizinIslamic University of Indonesia1
Arum Handini PrimandariIslamic University of Indonesia1
Muhammad BariklanaIslamic University of Indonesia1
Octavia RahmawatiIslamic University of Indonesia1
Nanda Lailatul HumairahAffiliation unknown1
Ashabul Akbar MaulanaIslamic University of Indonesia1
Alfa YulianaIslamic University of Indonesia1
Fitri AmaliaIslamic University of Indonesia1
Ika Meiza MaharaniIslamic University of Indonesia1
Mega Luna SuliztiaIslamic University of Indonesia1
Shazia Ayesha AzzahraIslamic University of Indonesia1
Wiranti Nugrah AndiniIslamic University of Indonesia1
Irwan SutisnaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM</dc:title>
	<dc:title xml:lang="id">Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM untuk Mendapatkan Estimasi Robust Regression Terbaik dalam Perkara Pidana di Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM</dc:title>
	<dc:creator xml:lang="en">Singgih, Malecita Nur Atala </dc:creator>
	<dc:creator xml:lang="en">Fauzan, Achmad</dc:creator>
	<dc:subject xml:lang="en">Regression analysis</dc:subject>
	<dc:subject xml:lang="en">Robust regression</dc:subject>
	<dc:subject xml:lang="en">M Estimation</dc:subject>
	<dc:subject xml:lang="en">S Estimation</dc:subject>
	<dc:subject xml:lang="en">MM Estimation.</dc:subject>
	<dc:description xml:lang="en">Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98
 </dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2022-01-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/18630</dc:identifier>
	<dc:identifier>10.20956/j.v18i2.18630</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 18 No. 2 (2022): JANUARY 2022; 251-260</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/18630/7614</dc:relation>
	<dc:relation>10.20956/j.v18i2.18630.g7614</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>