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All Journal JURNAL PENDIDIKAN TAMBUSAI Building of Informatics, Technology and Science Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Ilmu Multidisplin
Ramadhan, Andi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

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Komparasi Algoritma Neural Network dan K-Nearest Neighbor Dalam Mendeteksi Malware Android Ramadhan, Andi; Lindawati, Lindawati; Rose, Martinus Mujur
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3538

Abstract

The report from the State Cyber and Cryptography Agency (BSSN) recorded approximately 100 million cases of cyber attacks in Indonesia until April 2022, with ransomware and malware being the most commonly detected types of attacks. In this context, the increasingly sophisticated and hard-to-detect Android malware poses a serious threat, especially with successful penetrations into the Google Play Store. Therefore, early detection of Android malware is crucial. This research aims to compare the performance of two machine learning algorithms, Neural Network and K-Nearest Neighbors (KNN), in detecting malware on the Android platform. The dataset has been processed and divided into training and testing data. Both algorithms are trained using the training data, and their results are validated and evaluated. The research findings show that Neural Network achieves the best performance with an accuracy of 97%, precision of 97%, recall of 97%, and F1-score of 97%. Meanwhile, KNN performs slightly lower with an accuracy of 95%, precision of 96%, recall of 95%, and F1-score of 95%. In conclusion, Neural Network outperforms KNN in detecting Android malware based on accuracy and classification consistency. Further research suggestions involve the use of other algorithms, broader and more representative datasets, as well as the addition of features and parameter optimization. This research contributes to the development of accurate and effective solutions for detecting and identifying potentially harmful Android applications
Co-Authors Achmad Fauzi Adelia, Liza Ali Wafa Andina, Niyar Chandra, Jon Hendra Saputra Fitriyani, Fika Gilang Ramadhan Heikal, Jerry Hutapea, Yulisbet Lindawati Lindawati Martinus Mujur Rose Naomi Putri Manurung, Theresia Peronika Lumban Toruan, Enjelya Rachmat Irawan, Cahyo Sanjaya, Ferry