PENERAPAN SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE PADA PENGKLASIFIKASIAN KEJADIAN HUJAN KOTA PRABUMULIH MENGGUNAKAN METODE FUZZY NAÏVE BAYES DAN K-NEAREST NEIGHBOR

DITA, VIVI CLARA and Zayanti, Des Alwine and Cahyono, Endro Setyo (2024) PENERAPAN SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE PADA PENGKLASIFIKASIAN KEJADIAN HUJAN KOTA PRABUMULIH MENGGUNAKAN METODE FUZZY NAÏVE BAYES DAN K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

Imbalanced class data can affect classification performance. An imbalanced class occurs when the minority class is less than the majority class. Imbalanced class can be overcome by resampling one of them using synthetic minority over-sampling technique (SMOTE). This research aims to balance the class by applying SMOTE to the Prabumulih city rainfall event classification using the Fuzzy Naïve Bayes and K Nearest Neighbor methods. The data used is rainfall event data in Prabumulih city sourced from the visual crossing website with 17 variables, totalling 2556 data. Train data is 71,44% or 1826 data and test data is 28,56% or 730 data. The accuracy of rainfall event classification before the application of SMOTE using Fuzzy Naïve Bayes method resulted in accuracy, precision, recall, and f-score of 73,42%, 71,43%, 65,63%, and 68,40%, respectively. After the application of SMOTE using the Fuzzy Naïve Bayes method, the accuracy and precision decreased by 2,6% and 7,46%. In contrast, recall and f-score increased by 10,93% and 1,3%. Meanwhile, before the application of SMOTE using the K-Nearest Neighbor method, the accuracy, precision, recall, and f-score were 69,45%, 75,66%, 44,68%, and 69,93%. After the application of SMOTE using the K-Nearest Neighbor method, the accuracy, recall, and f-score increased by 3,84%, 26,57%, and 0,12%, respectively. On the other hand, precision decreased by 6,78%. The application of SMOTE using the K-Nearest Neighbor method has a better classification accuracy, seen from the accuracy, recall, and f-score values that increase compared to the application of SMOTE using the Fuzzy Naïve Bayes method.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Imbalanced class, SMOTE, Fuzzy Naïve Bayes, K-Nearest Neighbor
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q337.3 Swarm intelligence. Big data -- Social aspects. Information technology -- Economic aspects.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Vivi Clara Dita
Date Deposited:	03 Jun 2024 02:11
Last Modified:	03 Jun 2024 02:11
URI:	http://repository.unsri.ac.id/id/eprint/146170

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