Pemanfaatan Algoritma K-Means Untuk Mengetahui Sebaran Data Kecelakaan Lalu Lintas Kota Pekanbaru


  • Bela Oktavia Politeknik Caltex Riau


Traffic accidents are a problem that requires serious treatment, considering the loss caused. For this study, it is necessary to analyze the available data by mapping the distribution of traffic accident areas. According to the Satlantas Polresta Pekanbaru the number of accident cases in the Pekanbaru area reaches 20-30 every month for students, students, and workers. Stored data can be processed to get new knowledge. This new knowledge can be obtained using Clustering techniques. Clustering is intended to group data that has the same characteristics in the data set. One of the most popular clustering methods in the field of data mining is K-Means. K-Means is able to display data and group information about traffic-prone areas in the Pekanbaru area based on predetermined variables. The results of K-Means will describe the position of data distribution in real conditions using Algolia map. Based on testing that has been done using Black Box testing and User Acceptance Test, the results obtained by 100% with testing of 25 items. In addition, based on User Usability Testing, the results obtained were 93.1%, thus all features of the system have been implemented well so that it can assist the Satlantas Polresta Pekanbaru in analyzing data and has been able to meet the usability aspects