Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function
<div><p class="IEEEAbtract"><em>Semarang city is one of the busiest city in Indonesia. Doe to its role as the capital city of Central Java, Semarang is known as having a relativity high rate economic activities. The geographic of Semarang city bordered by the Java sea, th...
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doaj-9da261d423244bd2a783e48d69223dad2020-11-24T22:38:00ZengDiponegoro UniversityInternational Journal of Science and Engineering 2086-50232302-57432015-11-0191131610.12777/ijse.0.0.6995Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss FunctionAlan Prahutama0Hasbi Yasin1Statistics Department, Diponegoro UniversityStatistics Department, Diponegoro University<div><p class="IEEEAbtract"><em>Semarang city is one of the busiest city in Indonesia. Doe to its role as the capital city of Central Java, Semarang is known as having a relativity high rate economic activities. The geographic of Semarang city bordered by the Java sea, thus whenever the rainfall is high, there could be flood at certain area. Therefore, prediction of rainfall is very important. Support vector machine (SVM) is one of the most popular methods in nonlinear approach. One of the branches of this method for prediction is support vector regression (SVR). SVR can be approached by quadratic loss function. The study is focus on Semarang rainfall prediction during 2009 to 2013 using several kernel function. Kernel Function can provide optimal weight Some of kernel functions are linear, polynomial, and Radial Basis Function (RBF). Using this method, the study provide 71.61% R-square in the training data, for C parameter 2 with polynomial (p=2), and 71.46% R-square for the testing data</em><em> </em></p></div> <p> </p>http://ejournal.undip.ac.id/index.php/ijse/article/view/7941Rainfallsupport vector regression (SVR)kernel function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alan Prahutama Hasbi Yasin |
spellingShingle |
Alan Prahutama Hasbi Yasin Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function International Journal of Science and Engineering Rainfall support vector regression (SVR) kernel function |
author_facet |
Alan Prahutama Hasbi Yasin |
author_sort |
Alan Prahutama |
title |
Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function |
title_short |
Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function |
title_full |
Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function |
title_fullStr |
Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function |
title_full_unstemmed |
Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function |
title_sort |
prediction of weekly rainfall in semarang city use support vector regression (svr) with quadratic loss function |
publisher |
Diponegoro University |
series |
International Journal of Science and Engineering |
issn |
2086-5023 2302-5743 |
publishDate |
2015-11-01 |
description |
<div><p class="IEEEAbtract"><em>Semarang city is one of the busiest city in Indonesia. Doe to its role as the capital city of Central Java, Semarang is known as having a relativity high rate economic activities. The geographic of Semarang city bordered by the Java sea, thus whenever the rainfall is high, there could be flood at certain area. Therefore, prediction of rainfall is very important. Support vector machine (SVM) is one of the most popular methods in nonlinear approach. One of the branches of this method for prediction is support vector regression (SVR). SVR can be approached by quadratic loss function. The study is focus on Semarang rainfall prediction during 2009 to 2013 using several kernel function. Kernel Function can provide optimal weight Some of kernel functions are linear, polynomial, and Radial Basis Function (RBF). Using this method, the study provide 71.61% R-square in the training data, for C parameter 2 with polynomial (p=2), and 71.46% R-square for the testing data</em><em> </em></p></div> <p> </p> |
topic |
Rainfall support vector regression (SVR) kernel function |
url |
http://ejournal.undip.ac.id/index.php/ijse/article/view/7941 |
work_keys_str_mv |
AT alanprahutama predictionofweeklyrainfallinsemarangcityusesupportvectorregressionsvrwithquadraticlossfunction AT hasbiyasin predictionofweeklyrainfallinsemarangcityusesupportvectorregressionsvrwithquadraticlossfunction |
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