Open AccessANALISA CLUSTERING PADA DATA PELANGGARAN LALULINTAS DI PENGADILAN NEGERI DUMAI DENGAN MENGGUNAKAN METODE K-MEANS
Author(s) -
Elisawati Elisawati,
Deasy Wahyuni,
Adi Rio Arianto
Publication year - 2019
Publication title -
jiska (jurnal informatika sunan kalijaga)
Language(s) - English
Resource type - Journals
eISSN - 2528-0074
pISSN - 2527-5836
DOI - 10.14421/jiska.2019.42-01
Subject(s) - commit , cluster analysis , ticket , computer science , transport engineering , computer security , database , engineering , artificial intelligence
The order of traffic on the road is very important for motorists on the highway, the lack of awareness of motor vehicle users and the poor drivers of traffic discipline make the level of traffic violations in driving on the highway always increase so that the number of ticket data received by the Dumai District Court. This research was conducted to analyze and classify data violations using the k-means method to facilitate knowing the types of violations that are often violated by vehicle users. The attributes to be analyzed are the types of violations and types of vehicles. The test was carried out using the Rapidminer 5 application where the data tested was data from the Dumai District Court on December 2017, as many as 616 violations. Central cluster data consists of 3 clusters, namely C1 = Many, C2 = moderate and C3 = few who commit traffic violations. So the results of the data obtained where C1 produces 1 data, C2 gets as much as 4 data and C3 as many as 7 data. Where the type of violation that is often violated is the type of violation that does not use a helmet and the type of vehicle is a motorcycle. From the results of this study can be used or can be followed up with the holding of socialization to reduce the number of traffic violations. Keywords: Clustering Analysis, K-Means, Traffic Violations, Rapidminer