Pemodelan Tingkat Kriminalitas Di Indonesia Berdasarkan Faktor Sosial Ekonomi Menggunakan Geographically Weighted Regression

Damayanti, Ananda Nur (2026) Pemodelan Tingkat Kriminalitas Di Indonesia Berdasarkan Faktor Sosial Ekonomi Menggunakan Geographically Weighted Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Tingkat kriminalitas merupakan indikator penting dalam menilai stabilitas sosial dan keamanan suatu wilayah. Di Indonesia, kriminalitas menunjukkan distribusi yang tidak merata antarprovinsi sehingga mengindikasikan adanya pengaruh kondisi sosial ekonomi yang berbeda. Penelitian ini bertujuan untuk menganalisis faktor-faktor sosial ekonomi yang memengaruhi tingkat kriminalitas di Indonesia tahun 2024 menggunakan metode Geographically Weighted Regression (GWR). Data yang digunakan diperoleh dari Badan Pusat Statistika (BPS) dan Pusat Informasi Kriminalitas Nasional (PUSIKNAS) Bareskrim Kepolisian Republik Indonesia tahun 2024, dengan variabel yang digunakan meliputi indeks ratio gini, rata-rata lama sekolah, persentase penduduk miskin, tingkat pengangguran terbuka dan pertumbuhan ekonomi. Pemodelan dilakukan menggunakan fungsi pembobot Bisquare Fixed menunjukkan indeks rasio gini, rata-rata lama sekolah, persentase penduduk miskin dan tingkat pengangguran terbuka berpengaruh signifikan terhadap tingkat kriminalitas di Indonesia dengan koefisien determinasi sebesar 69,7%. Variabel-variabel tersebut memiliki pengaruh yang berbeda antarwilayah, sehingga mencerminkan adanya ketidakstasioneran spasial. Selain itu, jenis kriminalitas yang paling mendominasi secara nasional yaitu kasus narkotika dan penganiayaan.
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Crime rates are an important indicator in assessing the social stability and security of a region. In Indonesia, crime rates show an uneven distribution across provinces, indicating the influence of different socioeconomic conditions. This study aims to analyze the socioeconomic factors that influence crime rates in Indonesia in 2024 using the Geographically Weighted Regression (GWR). The data used was obtained from the Central Statistics Agency (BPS) and the National Crime Information Center (PUSIKNAS) of the Indonesian National Police in 2024, with variables including the Gini ratio index, average length of schooling, percentage of poor population, open unemployment rate, and economic growth. Modeling was performed using the Bisquare Fixed weighting function, showing that the Gini ratio index, average length of schooling, percentage of poor population, and open unemployment rate had a significant effect on crime rates in Indonesia with a coefficient of determination of 69.7%. These variables have different effects between regions, reflecting spatial non-stationarity. In addition, the most dominant types of crime nationally are narcotics and assault cases.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Geographically Weighted Regression, Indonesia, Kriminalitas, Sosial Ekonomi, Crime, Geographically Weighted Regression, Indonesia, Social Economi
Subjects:	H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis.
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Ananda Nur Damayanti Damayanti
Date Deposited:	09 Jan 2026 08:37
Last Modified:	09 Jan 2026 08:37
URI:	http://repository.its.ac.id/id/eprint/129425

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