Implementation of K-Means Clustering Algorithm for Grouping Traffic Violation Levels in Siak

Bias Arbi Fauzan STMIK Amik Riau
M Jamaris STMIK AMIK Riau
Junadhi Junadhi STMIK AMIK Riau

DOI: https://doi.org/10.36378/jtos.v5i1.2427

Keywords: K-Menas Clustering, Violation, Grouping, Traffic

Abstract

Traffic offences often occur in different regions, ranging from mild to moderate to severe. The categories of offences include not carrying a Driver's Licence, stnk (Vehicle Number Certificate) or stck (Vehicle Trial Certificate) is invalid, not wearing a seat belt, not turning on headlights during the day and under certain conditions, disobeying traffic signs, disobeying traffic signals. Moderate offences include not having a Driver's Licence, not concentrating while driving and breaking the door of the drawbar. Serious violations include deviating from other vehicles on the road, damaging and interfering with road functions, not insuring one's own responsibility and not insuring staff and passengers. In this study, the K-Means algorithm was used with the aim of obtaining information on data groups of traffic violations based on the time of the incident so that the cause of the traffic violations that occurred in Tasikmalaya City is known. Based on the validation with Davies Bouldin Index metric, 4 clusters were identified which can group the data well. The PerformanceVector results from the assessment of the clusters resulted in 4 clusters with a value of 0.134. Cluster 1 with the most data violations amounting to 74 violations occurred at night, Cluster 2 with the most violations amounting to 16 violations occurred during the day, Cluster 3 with the most violations amounting to 6 violations occurred in the afternoon and Cluster 4 with the most violations amounting to 113 violations occurred in the morning.

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Published

2022-06-29

Issue

Vol. 5 No. 1 (2022): Jurnal Teknologi dan Open Source, June 2022

Section

Articles

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