Pemetaan Jumlah Property Crime Di Provinsi Jawa Timur Menggunakan Metode Geographically Weighted Negative Binomial Regression (GWNBR) Dan Geographically Weighted Poisson Regression (GWPR)

Priambodo, Bagas Wahyu Yoga (2019) Pemetaan Jumlah Property Crime Di Provinsi Jawa Timur Menggunakan Metode Geographically Weighted Negative Binomial Regression (GWNBR) Dan Geographically Weighted Poisson Regression (GWPR). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Property crime merupakan kategori kejahatan yang termasuk di dalamnya yaitu pencurian, pengambilan sesuatu yang melanggar hukum, perampokan, pencurian kendaraan bermotor, kejahatan dengan pembakaran, dan perusakan properti. Ada beberapa faktor yang mempengaruhi tindakan kejahatan antara lain kemiskinan, kesempatan kerja, kepadatan penduduk, jumlah patroli polisi, keadaan jalan dan lingkungan, frekuensi ronda siskamling, dan faktor lainnya. Seringkali kondisi geografis wilayah yang terdapat kriminalitas mempengaruhi wilayah lain yang berada di sekitarnya. Untuk menyelesaikan kasus tersebut diperlukan suatu pemodelan dengan metode spasial kerena memperhatikan kondisi geografis yang ada di provinsi Jawa Timur. Kriteria AIC menunjukkan bahwa metode GWNBR merupakan metode yang paling sesuai untuk memodelkan jumlah kasus property crime setiap kabupaten/kota di Jawa Timur dibandingkan dengan metode regresi Poisson, regresi binomial negatif, dan GWPR.
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Property crime is a category of crime which includes theft, taking something that violates the law, robbery, motor vehicle theft, crime of burning, and destruction of property. Several factors influence crime, including poverty, employment opportunities, population density, number of police patrols, road conditions and the environment, frequency of patrolling, and other factors. Often the geographical conditions of areas that have crime affect other areas around them. To solve the case, modeling with a spatial method is needed because of the geographical conditions in the East Java province. The AIC criterion shows that the GWNBR method is the most suitable method for modeling the number of cases of property crime per district/city in East Java compared to Poisson regression methods, negative binomial regression, and GWPR.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Geographically Weighted Negative Binomial Regression, Geographically Weighted Poisson Regression, Kejahatan, Property Crime.
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis
Divisions:	Faculty of Mathematics, Computation, and Data Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Bagas Wahyu Yoga Priambodo
Date Deposited:	23 Jul 2026 03:12
Last Modified:	23 Jul 2026 03:12
URI:	http://repository.its.ac.id/id/eprint/65014

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