Abstract
Traffic accidents are one of the causes of high mortality in the community. Based on information from the World Health Organization (WHO) the number of accident victims in each year amounts to 1,300,000 fatalities, this is caused by traffic accidents that exist throughout the world. The police recorded data on accidents that occurred in several regions of East Priangan namely Ciamis and Tasikmalaya Regencies for the 2016-2017 period reaching an accident rate of ± 1500. The analysis that can be done to reduce the intensity of the occurrence of these events is to use data mining processing techniques. The right method is used by looking at the condition of the data obtained, namely the Association Rules method with the calculation of the Apriori Algorithm. This method will look for patterns of data relations that are formed from combinations of an itemset, so that knowledge will appear from large datasets. The pattern of the relationship sought is the linkages of itemset variables involved in the accident by involving 4 variables that describe the identity of the perpetrators, namely gender, age, profession and level of education and 22 attributes of the dataset. The minimum limit of support, confidence and lift ratio values used in the Apriori Algorithm calculation rules is 15%, 70% and 1.1. This value is used to get many rules that have a high level of occurrence accuracy. The results of the combination pattern calculation were 3 times iterations on each number of data in each region, the pattern of associations found in the Tasikmalaya region were the relation of the professional variables and the age of the perpetrator with the attribute of the Student profession dataset and the boundary group ages 16 to 30 years, while for the pattern associations found in the area of Ciamis Regency, namely the relation between age and education level with the attribute dataset of the 16 to 30 year age group and high school education level. The accuracy of the value obtained is calculated manually and uses one of the data mining applications as a comparison of value accuracy, namely Tanagra 1.4.
Highlights
Traffic accidents have become a social problem that is considered 'normal' for Indonesian people because the intensity of the events is getting higher
The area of Ciamis and Tasikmalaya Districts has become the attention of the police regarding traffic accidents, the police have recorded around 1500 cases of traffic accidents that occurred during the 2016-2017 period
Association Rules has a number of algorithm choices including the type of data mining association rules, one of the right algorithms with the aim of the research being done is using the Apriori Algorithm [6, 7]
Summary
Traffic accidents have become a social problem that is considered 'normal' for Indonesian people because the intensity of the events is getting higher. Statistical data shows that the number of traffic accidents nationally continues to increase every year. Association Rules is one of the data mining methods used to find relation patterns between data by determining association rules from data [3]. Method Association Rules has a number of algorithm choices including the type of data mining association rules, one of the right algorithms with the aim of the research being done is using the Apriori Algorithm [6, 7]. The results that will be obtained from the analysis process are the finding of a pattern of relations between accident data in the Tasikmalaya and Ciamis districts by using associative data mining rules [8]
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