PREDIKSI KEJADIAN HUJAN MENGGUNAKAN METODE ENSEMBLE DENGAN ALGORITMA FUZZY NAIVE BAYES, NAIVE BAYES DAN DECISION TREE BERDASARKAN RESAMPLING BOOTSTRAP

PANGESTI, AYU DWI and Resti, Yulia and Cahyono, Endro Setyo (2023) PREDIKSI KEJADIAN HUJAN MENGGUNAKAN METODE ENSEMBLE DENGAN ALGORITMA FUZZY NAIVE BAYES, NAIVE BAYES DAN DECISION TREE BERDASARKAN RESAMPLING BOOTSTRAP. Undergraduate thesis, Sriwijaya University.

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Abstract

Precise weather predictions are needed in various fields including agriculture, tourism, aviation, shipping and plantations. The most predictable weather element is rain. This study aims to predict rain events using the ensemble method with fuzzy naïve Bayes, naïve Bayes and decision tree algorithms based on bootstrap resampling. This study uses weather datasets in Australia sourced from the Kaggle website, totaling 145460 data. The level of accuracy obtained in predicting rain events using the fuzzy naïve Bayes method produces an average value of accuracy, precision, recall and fscore of 78.19%, 50%, 51.34% and 50.66%. The naïve Bayes method produces an average value of accuracy, precision, recall and fscore of 77.13%, 48.63%, 37.77% and 42.52%. The decision tree method produces an average value of accuracy, precision, recall and fscore of 76.66%, 48%, 50.90% and 49.41%. The ensemble method produces an average value of accuracy, precision, recall and fscore of 79.29%, 54.48%, 45.61% and 49.65%. The results show that the ensemble method produces better prediction accuracy because it produces higher accuracy and precision values compared to the other three methods. However, the naïve Bayes fuzzy method has a better recall and fscore than the other three methods.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pengetahuan Guru, Pemodelan Matematika
Subjects:	Q Science > Q Science (General) > Q1-295 General
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Ayu Dwi Pangesti
Date Deposited:	15 Jun 2023 06:10
Last Modified:	15 Jun 2023 06:10
URI:	http://repository.unsri.ac.id/id/eprint/110723

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