Associative Analysis Data Mining Pattern Against Traffic Accidents Using Apriori Algorithm

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.

Market basket analysis of administrative patterns data of consumer purchases using data mining technology

“…Antendecent is a group of items which have no common relationship in this analysis [18]. The association rules are developed in two stages, the following [21]:…”

“…The Apriori algorithm is a fundamental algorithm for the determination of frequency sets of the bolean regimes [21]. The main idea is to search for frequent item sets first (a set of items that meet minimum support).…”

“…Next, create itemset association rules, which meet the minimum data base confidence value [22]. The steps in the a priori algorithm [21]: a. High-frequency analysis (frequent item set) for item combinations that comply with the minimum support value requirements for transaction data..…”

Marketing strategy planning at alfamart lodadi stores using the clustering, ahp, and ar-mba method

“…2. Association Rule-Market Basket Analysis will find a pattern of data relations formed from a combination of an item set, so knowledge from a large dataset will appear (Ruswati, 2018). AR-MBA used to find out product items that are rarely, quite often, and often purchased by loyal customers and the best rules.…”

Solusi Prediksi Persediaan Barang dengan Menggunakan Algoritma Apriori (Studi Kasus: Regional Part Depo Auto 2000 Palembang)

“…Algoritma Apriori merupakan algoritma yang pertama kali digunakan ketika melakukan proses asosiasi untuk mengetahui hubungan item set apa saja yang terjadi pada proses transaksi [13]- [14]. Adapun penerapan algoritma apriori digunakan untuk menganalisis transaksi penjualan [15], pendidikan [16], [17], produksi [18], perbankan [19], transportasi [20], [21], dan Kesehatan [22].…”