Open AccessComputer Assisted Learning on Aridity Disaster Learning Using SIMIA (Satellite Imagery for Modelling Index of Aridity)
Author(s) -
Mila Chrismawati Paseleng,
Sri Yulianto Joko Prasetyo,
Kristoko Dwi Hartomo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1320/1/012029
Subject(s) - arid , index (typography) , plan (archaeology) , satellite , computer science , satellite imagery , vegetation (pathology) , aridity index , vegetation index , normalized difference vegetation index , remote sensing , environmental resource management , environmental science , geography , engineering , world wide web , geology , climate change , medicine , archaeology , pathology , aerospace engineering , paleontology , oceanography
The purpose of this study is to develop Computer Assisted Learning (CAL) to identify aridity using Satellite Imagery Modelling Index of Aridity (SIMIA) media. SIMIA is a system used to model area of aridity by using of rainfall data from observation of rain and vegetation index extracted by satellite image of LANDSAT 8 OLI and Google Satellite. SIMIA learning planning is performed using procedures of Analyzed, Standardized, Strategy, Utility, Require, and Evaluate (ASSURE). The results show that the ASSURE learning plan is more systematic, it attracts the attention of the participants; it can illustrate the goal to be achieved as determined by the learning achievements. The SIMIA CAL learning plan using ASSURE allows for the selection and delivery of appropriate, timely (pedagogical) content, the use of technology is appropriate to the needs of learners (technology) and the interaction is created between learners and instructors.