Open AccessComputational based on GUI MATLAB for back propagation method in detecting climate change: case study of mataram city
Author(s) -
Syaharuddin Syaharuddin,
Dessy Agustina Sari,
Johri Sabaryati,
Chandra Zonyfar,
S F Sihotang,
Ahmad Fadillah,
Tri Hariyati Nur Indah Sari,
Dwi Sudarno Putra,
Rina Rohayu Harun,
Vera Mandailina
Publication year - 2021
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 - 10.1088/1742-6596/1816/1/012001
Subject(s) - climate change , matlab , environmental science , maximum temperature , climatology , meteorology , computer science , geography , geology , oceanography , operating system
Climate change is a global phenomenon in which its impact can be perceived locally. In the face of this phenomenon, it is necessary to prepare and overcome this threat by developing various mitigation and adaptation strategies for climate change. To support this strategy, climate data such as temperature (maximum and minimum), rainfall, number of rainy days, as well as necessary information in the future need to be detected to prepare good and directional policies. The study aims to determine the climate parameters in Mataram city using forecasting by constructing the algorithm the Back Propagation method. In using the Matlab GUI application by analyzing climate change in Mataram city by using monthly data for the last 10 years. In the simulation stage, the data taken are the maximum temperature, minimum temperature, rainfall, and the number of rainy days in Mataram city. Based on the results of the training and testing process for climate data in the city of Mataram with a prediction of maximum temperature occurred in November at 32,58 0 C and maximum rainfall occurred in December as much as 399.2931 mm, while for the maximum number of rainy days occurred in January with a total of 21.99 days. These results can be used as an alternative policy in the handling and preparation of extreme climate change.