Performance Analysis of the Support Vector Machine Algorithm in Predicting Rain Potential in DKI Jakarta

Aina Latifa Riyana Putri

doi:10.59934/jaiea.v3i3.490

Authors

Aina Latifa Riyana Putri Institut Teknologi Telkom Purwokerto

DOI:

https://doi.org/10.59934/jaiea.v3i3.490

Keywords:

Support Vector Machine, Classification, Prediction, Confusion Matrix

Abstract

Indonesia is a country with a tropical climate which is located on the equator which tends to get sunlight throughout the year. Not only gives beauty but also saves disasters that can be dangerous such as floods, which usually occur due to high rainfall. The impact is also large on facilities with damage to buildings and health problems. It is very important to prepare it so that the possibility of damage or loss can be minimized. This research will apply the Support Vector Machine (SVM) Algorithm by selecting the distribution ratio of training and test data as well as the best kernel function to predict the potential for rain using daily climate data from the Meteorology, Climatology and Geophysics Agency (BMKG) in DKI Jakarta with the help of Rstudio software. The performance evaluated using the confusion matrix method produces the highest accuracy value of 89% is the SVM model with a training data distribution ratio of 90% and the Linear kernel as the chosen model for predicting rain potential.

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