ANALISIS PERBANDINGAN ALGORITMA KLASIFIKASI SUPPORT VECTOR MACHINE, DECISION TREE, NAÃVE BAYES BENCANA BANJIR BERDASARKAN CURAH HUJAN
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Institusion
Universitas Muhammadiyah Gorontalo
Author
Saleh, Nirmala
Subject
QA75 Electronic computers. Computer science
Datestamp
2024-08-15 06:09:07
Abstract :
Floods are one of the deadliest disaters in india, with the frequency of flooding and extreme rainfall events. Projected to recall under an increasingly warmer climate, this resarch aims to classify flood disasters based on rainfall and compare the level of accuracy of the Support Vector Machine, Decision Tree, and Naive Bayes algorithm. The result using the Suppor Vector Machine, Decision Tree, Naive Bayes algorithm with a total of 118 record will result in a Support Vector Machine accuracy value of 58.56%, and a Decision Tree accuracy rate of 52.98%, and a Naive Bayes accuracy rate of 56.33%. This the accuracy value of the three algorithms based on the largest accuracy results are in the Support Vector Machine of 58.56%.
Floods are one of the deadliest disaters in india, with the frequency of flooding and extreme rainfall events. Projected to recall under an increasingly warmer climate, this resarch aims to classify flood disasters based on rainfall and compare the level of accuracy of the Support Vector Machine, Decision Tree, and Naive Bayes algorithm. The result using the Suppor Vector Machine, Decision Tree, Naive Bayes algorithm with a total of 118 record will result in a Support Vector Machine accuracy value of 58.56%, and a Decision Tree accuracy rate of 52.98%, and a Naive Bayes accuracy rate of 56.33%. This the accuracy value of the three algorithms based on the largest accuracy results are in the Support Vector Machine of 58.56%.
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