Optimizing Imbalanced Data Classification: Under Sampling Algorithm Strategy with Classification Combination

Authors

  • Nauval Dwi Primadya Universitas Dian Nuswantoro
  • Adhitya Nugraha Universitas Dian Nuswantoro https://orcid.org/0000-0001-5366-110X
  • Sahrul Yudha Fahrezi Universitas Dian Nuswantoro
  • Ardytha Luthfiarta Universitas Dian Nuswantoro

DOI:

https://doi.org/10.31358/techne.v23i2.435

Keywords:

Random Undersampling, IoT Attacks 2023, Combination Algorithm

Abstract

The security of Internet of Things devices is a factor that must be considered because device damage and data theft can occur. Internet of Things devices are very useful in various sectors, such as health, transportation, and industrial sectors. Attacks on Internet of Things devices increase every year. To overcome this, it is necessary to take a research approach with machine learning. The dataset used is CIC IoT Attacks 2023 from the University Of New Brunswick. To be able to produce good data, it is necessary to do random under sampling as a way to overcome data imbalance. Then, modeling is done using the KNN algorithm, Random Forest, Logistic Regression, Adaboost, And Perceptron. The result of this research is that random forest has the best accuracy result of 99.73%. From these results, it can be concluded that the random under-sampling technique can improve the accuracy of data imbalance.

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Published

29-11-2024

How to Cite

Nauval Dwi Primadya, Adhitya Nugraha, Sahrul Yudha Fahrezi, & Ardytha Luthfiarta. (2024). Optimizing Imbalanced Data Classification: Under Sampling Algorithm Strategy with Classification Combination. Techné : Jurnal Ilmiah Elektroteknika, 23(2). https://doi.org/10.31358/techne.v23i2.435

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