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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Majalah Ilmiah MOMENTUM Demandia : Jurnal Desain Komunikasi Visual, Manajemen Desain, dan Periklanan Jurnal Teknologi Dan Sistem Informasi Bisnis JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat
Dwi Puji Prabowo
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Arts Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Jurnal Teknologi Dan Sistem Informasi Bisnis

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Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware Fauzi Adi Rafrastaraa; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Etika Kartikadarma; Usman Sudibyo
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.854

Abstract

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.
Co-Authors Ahmad Akrom Ajib Susanto Ali Muqoddas Alzami, Farrikh Christy Atika Sari De Rosal Ignatius Moses Dimas Irawan Ihya‘ Ulumuddin Eko Hari Rachmawanto Etika Kartikadarma Fauzi Adi Rafrastara Fikri Diva Sambasri Ibnu Utomo Wahyu Mulyono Megantara, Rama Aria Muslih Muslih Noor Hasyim Noor Hasyim Puri Sulistiyawati Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Usman Sudibyo Widyatmoko Karis Yundari, Yundari Yuslena Sari