Penerapan Metode Clustering dengan Fuzzy C-Means untuk Memetakan Daerah Rawan Kecelakaan Lalu Lintas di Surakarta
DOI:
https://doi.org/10.30646/sinus.v19i2.562Keywords:
Mapping, Traffic Accident, Fuzzy C-Means, Clustering Data, Silhouette IndexAbstract
Along with the development of Surakarta's infrastructure, the need for transportation has also increased. Indirectly, it will also cause several problems that must be considered, such as traffic accidents. Data regarding traffic accidents can be used to classify road sections based on the characteristic similarity factor inherent in the data. The sample data used were 1429 accident data from 89 road data in the city of Surakarta. The clustering method used to get the expected results is the Fuzzy C-Means method. The results of accident data grouping are displayed using tables and maps that describe the mapping of road sections in the jurisdiction of the Surakarta City Police. The variables used to cluster data are the number of events, the number of victims who died, and the number of injured victims. The result of this research is a system application that can classify accident-prone areas using the Fuzzy C-Means method into 3 clusters, where the first cluster consists of 5 data, the second cluster consists of 20 data, and the third cluster consists of 64 data.References
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