PENERAPAN K-NEAREST NEIGHBORS DAN RANDOM OVERSAMPLING PADA KLASIFIKASI KEJADIAN HUJAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

APRIANTI, KHOIRIYAH and Eliyati, Ning and Zayanti, Des Alwine (2024) PENERAPAN K-NEAREST NEIGHBORS DAN RANDOM OVERSAMPLING PADA KLASIFIKASI KEJADIAN HUJAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

Before performing classification, it is important to ensure that the dataset used does not contain missing data and imbalanced data. Missing data is a condition where some information or data from the dataset is not available. Imbalanced data is a condition where the number of observations in one class in a dataset is much greater than the number of observations in other classes. The purpose of this research is to classify rainfall events using linear SVM method by applying KNN (K=2) and ROS. The level of classification accuracy with imbalanced data produces an accuracy value of 83.29%, precision of 78.06%, and recall of 97.29%. While on balanced data by applying ROS produces an accuracy value of 92.74%, precision 100%, and recall 86.95%. The results showed that the application of ROS succeeded in increasing the accuracy value by 9.45% and precision by 21.94%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	KNN, Random Oversampling, SVM
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Khoiriyah Aprianti
Date Deposited:	23 Aug 2024 04:04
Last Modified:	23 Aug 2024 04:04
URI:	http://repository.unsri.ac.id/id/eprint/155419

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