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All Journal Jurnal Teknologi Terapan
Riyanto Riyanto
AMIK YMI Tegal
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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MODEL SUPPORT VEKTOR MACHINE (SVM) BERDASARKAN PARAMETER WINDOWS UNTUK PREDIKSI KEKUATAN GEMPA BUMI Oman Somantri; Santi Purwaningrum; Riyanto Riyanto
Jurnal Teknologi Terapan Vol 8, No 1 (2022): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v8i1.352

Abstract

Earthquakes are a type of natural disaster that currently cannot be predicted. Predicting the value of earthquake magnitude for related parties such as government and National Disaster Management Authority is very important. Furthermore, the results of earthquake predictions by several parties are used as indicators in post-earthquake response in minimizing the risks that will occur. Several studies have applied machine learning methods to predict earthquakes such as deep neural networks and parallel Support Vector Regression. In this article, we propose a data mining method using the Support Vector Machine (SVM) algorithm accompanied by the optimization of the windowing parameter value in the model that is applied to predict the value of the earthquake magnitude. Based on its advantages, the SVM model was chosen because it has been applicable in time series data processing. In the experimental stage process, parameter settings are first carried out, namely setting the kernel type, sampling type, and number of windowing to optimize the level of accuracy of the resulting model. The results showed that the best model with the smallest Root Mean Square Error (RMSE) was 0.712.
Co-Authors Oman Somantri Santi Purwaningrum