VISUALISASI DATA LOKASI RAWAN KECELAKAAN MENGGUNAKAN METODE CLUSTER ANALYSIS (STUDI KASUS: KOTA TANGERANG)

NURHIDAYATI, NOVIA (2024) VISUALISASI DATA LOKASI RAWAN KECELAKAAN MENGGUNAKAN METODE CLUSTER ANALYSIS (STUDI KASUS: KOTA TANGERANG). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Traffic accidents are a global issue that needs to be addressed seriously. Accidents can happen anywhere and anytime, making it difficult to determine which areas are prone to traffic accidents. Information about accident-prone areas is crucial for the community and law enforcement. This information can be used to monitor and anticipate accidents, especially by the police. This study was conducted to visualize accident-prone areas in the city of Tangerang. Every year, the city of Tangerang faces significant challenges with the continuous serious concern of traffic accident rates for the local government. Based on accident data from January 2022 to December 2023, there were a total of 1.496 accidents. The accident-prone locations are determined using GIS software. The method used is cluster analysis, where traffic accident data is grouped based on vulnerability levels. The kernel density method is then used to calculate the accident rate in each group. Keywords: Traffic accidents, Cluster Analysis, Kernel Densit Kecelakaan lalu lintas adalah masalah global yang perlu ditangani secara serius. Kecelakaan terjadi di mana saja dan kapan saja, sehingga sulit untuk menentukan daerah mana yang rawan kecelakaan lalu lintas. Informasi mengenai daerah rawan kecelakaan sangat penting bagi masyarakat dan penegak hukum. Informasi tersebut dapat digunakan untuk mengawasi dan mengantisipasi kecelakaan, terutama bagi kepolisian. Penelitian ini dilakukan untuk membuat visualisasi daerah rawan kecelakaan di Kota Tangerang. Setiap tahunnya Kota Tangerang menghadapi tantangan yang signifikan tingkat tingkat kecelakaan lalu lintas yang terus menjadi perhatian serius bagi pemerintah setempat. Berdasarkan data kecelakaan periode Januari 2022 hingga desember 2023 mencapai 1.496 jumlah kecelakaan. Lokasi rawan kecelakaan ditentukan dengan menggunakan perangkat lunak GIS. Metode yang digunakan adalah analisis klaster, di mana data kecelakaan lalu lintas dikelompokkan berdasarkan tingkat kerawanan. Metode kernel density kemudian digunakan untuk menghitung tingkat kecelakaan di setiap kelompok. Kata kunci: Kecelakaan lalu lintas, Cluster Analysis, Kernel Density

Item Type: Thesis (S1)
Call Number CD: FIK/SI. 24 126
Call Number: SIK/18/24/056
NIM/NIDN Creators: 41820010025
Uncontrolled Keywords: Kecelakaan lalu lintas, Cluster Analysis, Kernel Density
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.7 Data in Computer Systems/Data dalam Sistem-sistem Komputer > 005.74 Data Files and Database/Data File-file dan Database, Pangkalan Data, Pusat Data
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 022 Administration of The Physical Plant/Administrasi dan Fisik Perpustakaan > 022.1 Location and Site/Lokasi
100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 155 Differential and Developmental Psychology/Psikologi Diferensial dan Psikologi Perkembangan > 155.9 Environmental Psychology/Psikologi Lingkungan > 155.93 Influence of Specific Situations/Pengaruh Situasi Tertentu > 155.936 Accidents/Kecelakaan
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: khalimah
Date Deposited: 06 Aug 2024 07:09
Last Modified: 06 Aug 2024 07:09
URI: http://repository.mercubuana.ac.id/id/eprint/90041

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