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MODELLING CRIME RATES IN INDONESIA USING TRUNCATED SPLINE ESTIMATOR

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 2 (2024) · hlm. 1201–1216

DOI
10.30598/barekengvol18iss2pp1201-1216
ISSN
2615-3017, 1978-7227
Terbit
2024-05-25
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Criminal acts are actions that violate the law and can arise from various factors such as emotions, psychological pressure, and others. Crime rate is a number that indicate the level of crime vulnerability in a certain area at a certain time. Higher crime rates correspond to increased vulnerability in an area, and vice versa. Among various forms of criminal acts, the number of criminal acts and narcotics crimes in Indonesia tends to increase in 2020 and 2021. The aim of the research is to identify the characteristics of crime rate data based on the number of decency and narcotics incidents in Indonesia using a nonparametric regression approach. This research uses a nonparametric regression method spline truncated and linear regression as comparison. It was found that West Papua Province has the highest crime rate, based on a comparison between linear regression model and truncated spline nonparametric regression model, it can be concluded that the best model is the truncated spline nonparametric regression model with a Generalized Cross Validation (GCV) of 2468.487 and a coefficient of determination of 0.7389091, indicating that approximately 73% of the variability of the dependent variable can be explained by the independent variables included in the model.

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Toha SaifudinUniversitas Airlangga7
Juan Krisfigo PrasetyoAfiliasi tidak diketahui2
Berliani LarasatiAfiliasi tidak diketahui2
Bagas Shata PratamaAfiliasi tidak diketahui2
Dita AmeliaAfiliasi tidak diketahui2
Rien RecyliaAfiliasi tidak diketahui2
Belindha Ayu ArdhaniUniversitas Airlangga1
Nia SaurinaUniversitas Wijaya Kusuma Surabaya1
Riries RulaningtyasUniversitas Airlangga1
Aryati AryatiUniversitas Airlangga1
Rizky Ismaul Uyun HidayatAfiliasi tidak diketahui1
Mutiara AisharezkaAfiliasi tidak diketahui1
Naufal Ramadhan Al Akhwal SiregarAfiliasi tidak diketahui1
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Nina AuliyahAfiliasi tidak diketahui1
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Putu Eka AndrianiAfiliasi tidak diketahui1
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Qumadha Zaenal AbidinAfiliasi tidak diketahui1
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Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 5 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">MODELLING CRIME RATES IN INDONESIA USING TRUNCATED SPLINE ESTIMATOR</dc:title>
	<dc:creator>Juniar, Muhammad Althof</dc:creator>
	<dc:creator>Fania, Azzahra</dc:creator>
	<dc:creator>Ulya, Diana</dc:creator>
	<dc:creator>Ramadhan, Rico</dc:creator>
	<dc:creator>Chamidah, Nur</dc:creator>
	<dc:description xml:lang="en-US">Criminal acts are actions that violate the law and can arise from various factors such as emotions, psychological pressure, and others. Crime rate is a number that indicate the level of crime vulnerability in a certain area at a certain time. Higher crime rates correspond to increased vulnerability in an area, and vice versa. Among various forms of criminal acts, the number of criminal acts and narcotics crimes in Indonesia tends to increase in 2020 and 2021. The aim of the research is to identify the characteristics of crime rate data based on the number of decency and narcotics incidents in Indonesia using a nonparametric regression approach. This research uses a nonparametric regression method spline truncated and linear regression as comparison. It was found that West Papua Province has the highest crime rate, based on a comparison between linear regression model and truncated spline nonparametric regression model, it can be concluded that the best model is the truncated spline nonparametric regression model with a Generalized Cross Validation (GCV) of 2468.487 and a coefficient of determination of 0 .7389091, indicating that approximately 73% of the variability of the dependent variable can be explained by the independent variables included in the model.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-05-25</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12133</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss2pp1201-1216</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application; 1201-1216</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss2year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12133/7935</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Nur Chamidah, Azzahra Fania, Diana Ulya, Muhammad Althof Juniar, Rico Ramadhan</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>