Analisis Faktor-Faktor Laka Di Provinsi Jawa Timur Menggunakan Geographically And Temporally Weighted Regression

Falah, Syarif Fakhrul (2025) Analisis Faktor-Faktor Laka Di Provinsi Jawa Timur Menggunakan Geographically And Temporally Weighted Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kecelakaan lalu lintas dapat berdampak terhadap kerugian ekonomi dan kesejahteraan manusia. Oleh karena itu, penting untuk mengetahui faktor yang memengaruhi dan menentukan model yang sesuai, sehingga dapat dilakukan tindakan untuk menurunkan jumlah kecelakaan. Salah satu metode yang dapat menjawab permasalahan tersebut adalah pemodelan regresi. Terdapat tiga metode yang digunakan, yaitu Regresi Linier Berganda, Geographically Weighted Regression (GWR), dan Geographically and Temporally Weighted Regression (GTWR). Diantara ketiga metode tersebut diidentifikasi metode manakah yang paling baik dalam memodelkan jumlah kecelakaan lalu lintas. Data yang digunakan adalah jumlah kecelakaan lalu lintas di Provinsi Jawa Timur tahun 2021-2023 dan faktor yang diduga memengaruhinya, yaitu kepadatan jalan (X1), jumlah kendaraan bermotor (X2), kepadatan penduduk (X3), dan PDRB (X4). Penelitian ini diharapkan dapat mengetahui faktor yang memengaruhi jumlah kecelakaan lalu lintas dan dapat menjadi landasan dalam memberikan kebijakan. Berdasarkan penelitian yang telah dilakukan, GWR menjadi metode yang dipilih karena mampu menjelaskan variabilitas kecelakaan lalu lintas dengan baik serta residual model yang berdistribusi normal. Menggunakan metode tersebut faktor yang memengaruhi jumlah laka di setiap kabupaten/kota berbeda. Terdapat wilayah yang dipengaruhi jumlah kendaraan bermotor atau PDRB saja. Disisi lain, terdapat wilayah yang dipengaruhi beberapa variabel yang digunakan dengan kombinasi yang bervariasi. Kemudian terdapat pula wilayah yang tidak dipengaruhi oleh variabel yang digunakan pada penelitian ini.
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Traffic accidents can have an impact on economic losses and human welfare. Therefore, it is important to know the influencing factors and determine the appropriate model, so that actions can be taken to reduce the number of accidents. One of the methods that can answer this problem is regression modeling. There are three methods used, namely Multiple Linear Regression, Geographically Weighted Regression (GWR), and Geographically and Temporally Weighted Regression (GTWR). Among the three methods, it was identified which method was the best in modeling the number of traffic accidents. The data used are the number of traffic accidents in East Java Province in 2021-2023 and the factors that are suspected of influencing it, namely road density (X1), number of motorized vehicles (X2), population density (X3), and GDP (X4). This research is expected to find out the factors that affect the number of traffic accidents and can be the basis for providing policies. Based on the research that has been conducted, GWR is the method of choice because it is able to explain the variability of traffic accidents well and the residual model is normally distributed. Using this method, the factors that affect the number of laka in each district/city are different. There are areas that are affected by the number of motor vehicles or GDP only. On the other hand, there are areas that are influenced by several variables that are used in a variety of combinations. Then there are also areas that are not affected by the variables used in this study.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Multiple Linier Regression, Geographically Weighted Regression, Geographically and Temporally Weighted Regression, Traffic Accidents, Regresi Linier Berganda, Geographically Weighted Regression, Geographically and Temporally Weighted Regression, Kecelakaan Lalu Lintas
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. H Social Sciences > HE Transportation and Communications > HE5614.3.N5 Traffic accidents
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Syarif Fakhrul Falah
Date Deposited:	08 Apr 2025 05:19
Last Modified:	08 Apr 2025 05:19
URI:	http://repository.its.ac.id/id/eprint/118999

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