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Prediksi Kerawanan Wilayah Terhadap Tindak Pencurian Sepeda Motor Menggunakan Metode (S)ARIMA Dan CART

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 11 No. 2 (2017) · pp. 119–130

DOI
10.22146/ijccs.16643
ISSN
2460-7258, 1978-1520
Published
2017-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Motor vehicle theft is a crime that is most common in Indonesia. Growth of vehicle motorcycle significant in each year accompanied by the increasing theft of motorcycles in each year, we need a system that is able to forecast the development and the theft of the motorcycle.This research proposes the development of forecasting models vulnerability criminal offense of theft of motorcycles with ARIMA forecasting method. This method not only forecast from variable of theft but also residents, vehicles and unemployment. The study also determined the classification level of vulnerability to the crime of theft of a motorcycle using a method based on the Decision Tree CART ARIMA forecasting method.Forecasting time series data with ARIMA method performed by each of the variables to produce the best ARIMA forecasting model which varies based on the data pattern of each of those variables. The results of classification by CART method to get the value of accuracy of 92% for the city of Yogyakarta and 85% for DIY. Based on the above, the results of ARIMA forecasting and classification CART can be used in determining the level of vulnerability to the crime of theft of motorcycles.

Keywords

Collaboration network of the authors

25 authors · 32 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Universitas Jambi
  • Muhammadiyah University of Surakarta
  • Universitas Maritim Raja Ali Haji
  • Malikussaleh University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus SihabuddinUniversitas Gadjah Mada2
Harliana HarlianaAffiliation unknown1
Bhakti SusiloAffiliation unknown1
Mutammimul UlaMalikussaleh University1
Harry Ganda NugrahaUniversitas Gadjah Mada1
Izmy Alwiah MusdarUniversitas Gadjah Mada1
Devid Haryalesmana WahidUniversitas Gadjah Mada1
Victor PaskalathisUniversitas Gadjah Mada1
Azizah FatmawatiMuhammadiyah University of Surakarta1
Nisa RNAMuhammadiyah University of Surakarta1
Muhammad FahrurroziUniversitas Gadjah Mada1
Ferdi ChahyadiUniversitas Maritim Raja Ali Haji1
Hendra KurniawanUniversitas Maritim Raja Ali Haji1
Sigit PriyantaUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Abram Setyo PrabowoUniversitas Gadjah Mada1
Yayang Eluis Bali MawartikaUniversitas Gadjah Mada1
Muh Amin NurrohmatUniversitas Gadjah Mada1
Taufan Bagus Dwi Putra AditamaUniversitas Gadjah Mada1
Fauzan IshlakhuddinUniversitas Gadjah Mada1
Benedika Ferdian HutabaratUniversitas Jambi1
Indra WeniUniversitas Jambi1
Muksin AlfalahUniversitas Jambi1

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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id-ID">Prediksi Kerawanan Wilayah Terhadap Tindak  Pencurian Sepeda Motor Menggunakan  Metode (S)ARIMA Dan CART</dc:title>
	<dc:creator>Utomo, Pradita Eko Prasetyo</dc:creator>
	<dc:creator>SN, Azhari</dc:creator>
	<dc:subject xml:lang="id-ID"></dc:subject>
	<dc:subject xml:lang="id-ID">ARIMA, CART, vulnerability, Forecasting, Decision Tree</dc:subject>
	<dc:description xml:lang="id-ID">Motor vehicle theft is a crime that is most common in Indonesia. Growth of vehicle motorcycle significant in each year accompanied by the increasing theft of motorcycles in each year, we need a system that is able to forecast the development and the theft of the motorcycle.This research proposes the development of forecasting models vulnerability criminal offense of theft of motorcycles with ARIMA forecasting method. This method not only forecast from variable of theft but also residents, vehicles and unemployment. The study also determined the classification level of vulnerability to the crime of theft of a motorcycle using a method based on the Decision Tree CART ARIMA forecasting method.Forecasting time series data with ARIMA method performed by each of the variables to produce the best ARIMA forecasting model which varies based on the data pattern of each of those variables. The results of classification by CART method to get the value of accuracy of 92% for the city of Yogyakarta and 85% for DIY. Based on the above, the results of ARIMA forecasting and classification CART can be used in determining the level of vulnerability to the crime of theft of motorcycles.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="id-ID"></dc:contributor>
	<dc:date>2017-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/16643</dc:identifier>
	<dc:identifier>10.22146/ijccs.16643</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 11, No 2 (2017): July; 119-130</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/16643/16676</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>