Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware

  • Fauzi Adi Rafrastaraa Universitas Dian Nuswantoro
  • Ricardus Anggi Pramunendar Universitas Dian Nuswantoro
  • Dwi Puji Prabowo Universitas Dian Nuswantoro
  • Etika Kartikadarma Universitas Dian Nuswantoro
  • Usman Sudibyo Universitas Dian Nuswantoro
Keywords: Random forest, principal component analysis, features reduction, malware detection


Malware is a type of software designed to harm various devices. As malware evolves and diversifies, traditional signature-based detection methods have become less effective against advanced types such as polymorphic, metamorphic, and oligomorphic malware. To address this challenge, machine learning-based malware detection has emerged as a promising solution. In this study, we evaluated the performance of several machine learning algorithms in detecting malware and applied Principal Component Analysis (PCA) to the best-performing algorithm to reduce the number of features and improve performance. Our results showed that the Random Forest algorithm outperformed Adaboost, Neural Network, Support Vector Machine, and k-Nearest Neighbor algorithms with an accuracy and recall rate of 98.3%. By applying PCA, we were able to further improve the performance of Random Forest to 98.7% for both accuracy and recall while reducing the number of features from 1084 to 32.


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How to Cite
Rafrastaraa, F. A., Pramunendar, R. A., Prabowo, D. P., Kartikadarma, E., & Sudibyo, U. (2023). Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware. Jurnal Teknologi Dan Sistem Informasi Bisnis, 5(3), 217-223. https://doi.org/10.47233/jteksis.v5i3.854