Traffic Violation Clustering Using K-Medoids and Word Cloud Visualization

Traffic is the space for people to move around, including both drivers and pedestrians. According to data from the Central Statistics Agency in 2020, the number of motor vehicles in Makassar City was recorded by type: 248,682 passenger cars, 17,501 buses, 85,968 trucks, and 1,338,306 motorcycles, with a tendency for an increase in the following year. The high number of vehicle users can certainly affect the rising traffic violation rates on the road. This study aims to classify traffic violation types in Makassar City by utilizing the K-Medoids algorithm and to visualize the clustering results using Word Cloud, which is expected to provide information related to patterns of traffic violation clusters. This study uses a case study from the Traffic Police Department of Makassar City in 2021, with a total of 5,893 traffic violation cases. The data used is ticket data consisting of article and vehicle type features. The clustering results show that motorcycles and minibuses are the most frequently involved in traffic violations. Motorcycles (R2) are not only dominated by violations related to the use of standard SNI helmets but also significantly involved in violations related to incomplete requirements and the possession of SIM/STNK (Driver's License/Vehicle Registration) and failing to meet roadworthiness standards such as mirrors, headlights, horns, etc. Passenger vehicles, especially minibuses and cars, also dominate traffic violations. The violations involve not only the use of seat belts for R4 vehicles but also violations such as not having complete STNK, not being able to show SIM, failing to display the Vehicle Registration Mark (TKB), and others. The results of this study demonstrate that the clustering obtained is very strong, as evidenced by the high Silhouette Score of 0.867 at k = 9.

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Publication Details

Journal
Journal of Information Systems and Informatics
Published
2025-03-20
DOI
https://doi.org/10.51519/journalisi.v7i1.1002
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
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article

Traffic Violation Clustering Using K-Medoids and Word Cloud Visualization

Ema Utami, Muhammad Sabri S
Journal of Information Systems and Informatics
Traffic Prediction and Management Techniques
article

Traffic Violation Clustering Using K-Medoids and Word Cloud Visualization

Ema Utami, Muhammad Sabri S
article en

Abstract

Traffic is the space for people to move around, including both drivers and pedestrians. According to data from the Central Statistics Agency in 2020, the number of motor vehicles in Makassar City was recorded by type: 248,682 passenger cars, 17,501 buses, 85,968 trucks, and 1,338,306 motorcycles, with a tendency for an increase in the following year. The high number of vehicle users can certainly affect the rising traffic violation rates on the road. This study aims to classify traffic violation types in Makassar City by utilizing the K-Medoids algorithm and to visualize the clustering results using Word Cloud, which is expected to provide information related to patterns of traffic violation clusters. This study uses a case study from the Traffic Police Department of Makassar City in 2021, with a total of 5,893 traffic violation cases. The data used is ticket data consisting of article and vehicle type features. The clustering results show that motorcycles and minibuses are the most frequently involved in traffic violations. Motorcycles (R2) are not only dominated by violations related to the use of standard SNI helmets but also significantly involved in violations related to incomplete requirements and the possession of SIM/STNK (Driver's License/Vehicle Registration) and failing to meet roadworthiness standards such as mirrors, headlights, horns, etc. Passenger vehicles, especially minibuses and cars, also dominate traffic violations. The violations involve not only the use of seat belts for R4 vehicles but also violations such as not having complete STNK, not being able to show SIM, failing to display the Vehicle Registration Mark (TKB), and others. The results of this study demonstrate that the clustering obtained is very strong, as evidenced by the high Silhouette Score of 0.867 at k = 9.

Journal of Information Systems and InformaticsVol. 7(1)
Universitas Respati Yogyakarta (ID), Universitas Amikom Yogyakarta
Openalex Percentile: Top 93%
Traffic Prediction and Management Techniques
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