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ANALISIS MODEL CLUSTERING KECELAKAAN LALU LINTAS DI KOTA PALEMBANG MENGGUNAKAN PENDEKATAN MACHINE LEARNING BERDASARKAN DATA SEKUNDER DARI KEPOLISIAN RESOR KOTA BESAR PALEMBANG TAHUN 2020 - 2022

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Between 20 and 50 million people suffer non-fatal injuries in road accidents every year, while more than a million of these incidents cause death. Road traffic crashes are a significant menace not only to the economy but also to public health. The numbers obtained are objective statistics not subject of personal opinion. This research aims to develop a clustering model using a machine learning approach based on characteristics of incidents and number of victims in traffic accidents in Palembang City, South Sumatra Province, Indonesia. Research will analyse the pattern which observes different relationships among accidents using various clustering techniques such as K-means clustering, Gaussian mixture models (GMM), density-based (DBSCAN), hierarchical clustering, spectral clustering, and OPTICS (ordering points to identify the clustering structure). The result shows that the algorithm called spectral clustering outperforms others with a low Davies-Bouldin score (0.3221), a high Calinski-Harabasz score (14789.9374), and a silhouette coefficient (0.7695). Spectral clustering was found to be the best algorithm out of the six algorithms evaluated for this paper. Keywords: Traffic Accidents, Clustering Model, Machine Learning, Silhouette Coefficient, Davies Bouldin Index, Calinski-Harabasz

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Inventory Code Barcode Call Number Location Status
2407004261T150543T1505432024Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1505432024
Publisher
Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiii, 50 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.310 7
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Machine Learning
Prodi Sistem Komputer
Specific Detail Info
-
Statement of Responsibility
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