Perbandingan Data Mining Mengidentifikasi Pola Keterkaitan Variabel Kecelakaan Lalu Lintas Di Polresta Kota Medan - Rusmin Saragih; Juliana Naftali Sitompul (UNIVERSITAS PELITA HARAPAN, 2019)
This research was conducted to analyze the results of data mining processing on a priori method and the method of k-means clustering in analyzing the comparison of the two methods to factors related to the incidence of traffic accidents that occurred in the POLRESTA Medan area. Analysis of the pattern of the causes of traffic accidents conducted in this study using Apriori data mining methods and k-means clustering. Apriori method is a data mining method that produces association patterns or linkages between variables or itemset based on frequent or frequent itemset. While the k-means clustering method is a method that groups data into different groups so that data with certain patterns will form their respective groups. By using a priori and k-means clustering, a comparative analysis can be obtained between the two methods. This research was carried out by collecting data on traffic accidents obtained from POLRESTA Medan followed by the development of a data mining software that implements the Apriori method and k-means clustering to produce the association and clustering patterns contained in the accident data. The results of the comparison between the two methods can then be information and references to the performance of the two methods in processing traffic accident data in POLRESTA Medan. Keywords: Accidents, Traffic, POLRESTA Medan, data mining, a priori, k-means.