Implementation Of Purity K-Means Algorithm In Accident Data Clustering In North Padang Lawas District - Khopipah Parawansah Siregar; Bustami Bustami; Sujacka Retno (Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN), 2024)
Traffic safety is an important issue, especially in areas with high accident rates, such as North Padang Lawas Regency in North Sumatra. This study uses the K-Means Purity Algorithm to group regions based on the level of vulnerability to traffic accidents. The data analyzed includes the number of accidents, deaths, serious injuries, and minor injuries from 2019 to 2023. The results of clustering show that some sub-districts have fluctuating levels of vulnerability. Batang Onang District, for example, was categorized as "Not Vulnerable" in 2019 and 2021, but increased to "Vulnerable" in 2020, 2022, and 2023, indicating a spike in risk. In contrast, Dolok District is mostly in the "Not Vulnerable" category, except in 2023. East Halongonan sub-district is almost always in the "Vulnerable" category, indicating a consistently high risk, while Hulu Sihapas and Simangambat experience fluctuations in vulnerability levels from year to year. Ujung Batu, which is generally classified as "Not Vulnerable," indicates an increased risk in certain years. In conclusion, the K-Means algorithm successfully maps accident-prone areas, providing important insights for more effective interventions. This information can help the government in designing better road safety strategies, such as infrastructure improvements and traffic safety awareness campaigns, to reduce future accidents. Keywords: Traffic Accidents, K-Means Purity Algorithm, Data Mining, North Padang Lawas, Accident Zoning