Open AccessApplication of Fuzzy C-Means in Level Clustering of Traffic Accident Vulnerability
Author(s) -
Khalida Syahputri,
Rahayu Mayang Sari,
I Rizkya,
T. A. Farhan,
O. C. Syardhi
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1003/1/012099
Subject(s) - traffic accident , vulnerability (computing) , fuzzy logic , cluster analysis , transport engineering , computer science , road traffic accident , road accident , fuzzy clustering , road traffic , data mining , geography , computer security , engineering , artificial intelligence
Traffic accidents are a serious problem and need the right handling. The high level of traffic accidents is a problem often happened in big cities, especially in the Medan city. Data on traffic accident of 2018 are obtained from the Medan Police Station Unit and from the official website of Badan Pusat Statistika recorded of 1393 traffic accidents with 254 deaths, 721 seriously injured, and 985 slightly injured. Then this data grouped based on the accident road segment and culestered level of vulnerability using the Fuzzy C-Means Algorithm. The results of the Fuzzy C-Means algorithm are 13 road segments in vulnerable clusters, 36 road segments in quite vulnerable clusters and 53 road segments in safe clusters. Road segments in vulnerable clusters are marked in red, quite vulnerable clusters are marked in yellow and safe culsters are in green. Data validation obtained by the traffic accident vulnerability clustered by classifying traffic accident data in the first two months of 2019 using the same method, namely the Fuzzy C-Means Algorithm, the conformity level of the Fuzzy C-Means resulted of 78,75%.