Skripsi

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

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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%.

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Detail Information

Series Title

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Call Number

T1554192024

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2024

Collation

xv, 42 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Ilmu Matematika
Pembelajaran Mesin (Machine Learning)

Specific Detail Info

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Statement of Responsibility

TUTI

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