KLASTERISASI DATA KECELAKAAN LALU LINTAS JALAN RAYA DI PROVINSI JAMBI MENGGUNAKAN METODE K-MEANS

DAHLAN, BULAN FITRI and Kurniati, Rizki and Rodiah, Desty (2024) KLASTERISASI DATA KECELAKAAN LALU LINTAS JALAN RAYA DI PROVINSI JAMBI MENGGUNAKAN METODE K-MEANS. Undergraduate thesis, Sriwijaya University.

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Abstract

Traffic accidents are one of the main problems faced by many regions, including Jambi Province, where accidents often result in losses both in terms of casualties and material. This research focuses on applying the K Means clustering method to analyze traffic accident data in Jambi Province. The analyzed data are from 2018 to 2022, obtained from the Traffic Directorate of Jambi Province and Muaro Jambi Police Station, and involves data collection, preparation, and preprocessing processes. Data clustering produced seven clusters for the accident data dataset with the smallest value of 0.1202 as the optimal number of clusters, while the incident location data dataset produced 6 clusters with the smallest DBI value of 0.3945 as the optimal number of clusters. This confirms the effectiveness of the K Means method in traffic accident analysis.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Jambi Province, K Means clustering, risk analysis, traffic accidents
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.76.I58.A3115 Computer science. Computers. Intelligent agents (Computer software) Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Bulan Fitri Dahlan
Date Deposited:	29 Feb 2024 01:59
Last Modified:	29 Feb 2024 01:59
URI:	http://repository.unsri.ac.id/id/eprint/141182

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