Open AccessPENGELOMPOKAN KEJADIAN GEMPA BUMI MENGGUNAKAN FUZZY C-MEANS CLUSTERING
Author(s) -
Ryan Rifqi Arista,
Rosa Andrie Asmara,
Dian Puspitasari
Publication year - 2019
Publication title -
j-tit : jurnal teknologi informasi dan terapan/j-tit (jurnal teknologi informasi dan terapan)
Language(s) - English
Resource type - Journals
eISSN - 2580-2291
pISSN - 2354-838X
DOI - 10.25047/jtit.v4i2.67
Subject(s) - cluster analysis , computer science , vulnerability (computing) , seismology , data mining , geology , artificial intelligence , computer security
The Indonesian region has a high level of earthquake vulnerability when compared to other countries. This is because Indonesia's position is at the confluence of three large tectonic plates namely the Eurasian plate, the Indo-Australian plate, and the Pacific plate. The high level of earthquake susceptibility is evidenced by significant earthquake data from 2005 to 2009, which recorded 26 significant earthquakes over a period of 4.8 to 8.6 on the Richter scale. The earthquake also caused impacts including casualties, injuries, damage to houses and destruction of houses.The earthquake event grouping system is a system that functions to classify earthquake events based on two main parameters, namely earthquake strength parameters and earthquake impact parameters. The two parameters are grouped separately, so that the grouping process produces two kinds of grouping results. The stages of this system start from preprocessing data to eliminate noise, then take grouping parameters from the user in the form of the number of clusters, minimum error values, and the maximum iteration limit. Grouping is done using fuzzy c-means method. The grouping results are then displayed in table form and in the form of coordinates in Google Maps.The grouping of earthquake events has been tested by comparing the results of grouping systems with the results of manual grouping. Testing is done by inputting a number of different maximum iterations. Based on the test results it was found that the greater the maximum iteration value will affect the accuracy of grouping.