K-MEANS CLUSTERING AREAS PRONE TO TRAFFIC ACCIDENTS IN ASAHAN REGENCY

  • Nurul Rahmadani (1*) Sekolah Tinggi Manajemen Informatika dan Komputer Royal
  • Elly Rahayu (2) Sekolah Tinggi Manajemen Informatika dan Komputer Royal
  • Ayu Lestari (3) Sekolah Tinggi Manajemen Informatika dan Komputer Royal

  • (*) Corresponding Author
Keywords: Accident Prone Areas, K-Means Clustering, Traffic Accident

Abstract

Traffic accidents on the highway still contribute to the high mortality rate in Indonesia, so it is of particular concern to the police in this country. Accidents occur in various places with different time events, this makes it difficult to determine which areas have a high level of traffic accident vulnerability. Information about traffic accident-prone areas is needed by the community and law enforcement. This information can be taken into consideration for supervision and anticipatory action, especially for the police. The initial stage of traffic accident prevention is to know the factors that cause traffic accidents obtained through traffic accident data analysis. The information system in this study analyzed traffic accident-prone areas in Asahan Regency. The analysis can be done with data mining, namely K-Means Clustering which can group data into several groups according to the characteristics of the data. The results of this study are the Asahan District Police Satlantas can find out the accident-prone areas in the most vulnerable categories, quite vulnerable and not vulnerable.

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Published
2021-02-02
How to Cite
[1]
N. Rahmadani, E. Rahayu, and A. Lestari, “K-MEANS CLUSTERING AREAS PRONE TO TRAFFIC ACCIDENTS IN ASAHAN REGENCY”, jitk, vol. 6, no. 2, pp. 181-186, Feb. 2021.
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