DSpace logo

Please use this identifier to cite or link to this item: http://repository.uksw.edu/handle/123456789/13367
Title: Detection Model of Landslide-Potential Areas based on Local-Learning using Iterative Dichotomiser Three Algorithm
Authors: Susetyo, Yerymia Alfa
Keywords: ID3 Algorithm;landslide;land use;learning algorithm;local geographic
Issue Date: 2016
Publisher: Magister Sistem Informasi Program Pascasarjana FTI-UKSW
Abstract: Abstract— Landslide is the most destructive natural disaster since it causes very significant environmental and socioeconomic damages. Java, Indonesia is the most densely populated island in the world. High population density and careless land conversion lead to frequent landslides. Landslide itself is the most frequent natural disaster in Indonesia. This research aims to develop an early warning model of landslide-potential areas based on local-learning that suits local geographical conditions using Iterative Dichotomiser Three (ID3) in Java as the most landslide-prone area in Indonesia. We analyze and map landslide data with climate and soil characteristics using ID3 algorithm. In this research, we utilize landslide-causing attributes i.e. area slope, rainfall, soil type, and land cover. This research produce 36 leaf-node decision tree, where 19 leaf-node indicate “Landslide-potential” and 17 leaf-node points to “Not Landslide-potential”. Furthermore, the accuracy level of this model is 92.37% with land cover attribute is the main attribute that trigger landslide.
URI: http://repository.uksw.edu/handle/123456789/13367
Appears in Collections:T2 - Master of Information Systems

Files in This Item:
File Description SizeFormat 
T2_972014001_Full text.pdfFull text2.65 MBAdobe PDFView/Open
T2_972014001_Abstract.pdfAbstract353.69 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.