IMPLEMENTASI DATA MINING DENGAN METODE CLUSTERING ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DATA TILANG DI POLDA KEPRI
Abstract
Every community relies on transportation, but drivers must follow the rules to be safe. There are several variables that lead many individuals to get ticketed, including the public's lack of knowledge and awareness of excellent, accurate, and safe driving standards, and the community's failure to check vehicle conditions and paperwork before traveling to avoid tickets during special operations (raids). This research examined two-wheeled drivers' traffic offenses in Batam City, an issue investigated by the author due to the numerous traffic offences that have disrupted the regulatory system thus far. The K-Means method clusters. Implementing the K-Means Algorithm to group traffic violation data helps the Riau Regional Police discover the most traffic infractions and the ticketing service department locate Batam traffic violation data groupings. The author will utilize a K-Means algorithm data mining approach to segment infractions thus far. Data analysis from the cluster, segregated by mopabudget type, degree of violation, and fine amount, yielded the findings of Cluster 0: 11 items 13. Cluster 2: 11 items from 35 data treated as sample data with performance vector; best clustering is 0.499.
Keywords : Transport, Traffic Violation, Data Mining, K-Means Algorithm, cluster, RapidMiner
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