Integrasi Machine Learning dan Pendekatan Humanistic dalam Deteksi Dini Serangan Siber untuk Keamanan Digital
Main Article Content
Abstract
The rapid development of digital technology has given rise to new challenges in the form of an increase in complex cyberattacks that are difficult to detect manually. This study aims to analyze the application of machine learning algorithms in early cyberattack detection as an effort to strengthen digital security systems. The method used in this study is a systematic literature review of IEEE and Scopus indexed scientific publications for the 2020–2025 period. The analysis focuses on the effectiveness of Support Vector Machine (SVM), Random Forest, and Deep Neural Network (DNN) algorithms in recognizing attack patterns such as phishing, malware, and DDoS. The results of the study indicate that machine learning has high potential to increase the speed and accuracy of cyberthreat detection, with an average accuracy rate of over 90% across various datasets. However, the successful implementation of this technology is highly dependent on the availability of quality data and users' understanding of digital ethics. This study concludes that the integration of artificial intelligence and humanistic awareness is key to building a safe, adaptive, and ethical digital ecosystem.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
References
Al Ghifari, M.G. (n.d.) Prediksi dropout siswa dengan kecerdasan buatan yang dapat dijelaskan (Explainable AI) menggunakan SHAP dan machine learning.
Arafat, M. and Wirasto, A.T.E. (2024) ‘Kebijakan kriminal dalam penanganan siber di era digital: Studi kasus di Indonesia’, Equal: Journal of Law and Justice, 1(2), pp. 220–241.
Kurnianto, A.F. (2024) Implementasi Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM) pada Pengenalan Tokoh Wayang Kulit Berbasis Android. PhD Thesis. Universitas Pembangunan Nasional “Veteran” Jawa Timur.
Rifai, M.H., Pramudya, D.A. and Narfandi, R.R. (2024) ‘Analisis peran teknologi kecerdasan buatan dalam mengoptimalkan proses deteksi terhadap serangan siber’, in Prosiding Seminar Nasional Teknologi Informasi dan Bisnis, pp. 495–502.
Risyani, Y. et al. (2025) ‘Sistem keamanan siber adaptif berbasis AI: Analisis kinerja, arsitektur, dan penerapannya pada organisasi modern’, Jurnal Minfo Polgan, 14(2), pp. 2999–3006.
Rustiyana, R., Judijanto, L., Supartha, I.K.D.G. and Gunawan, P.W. (2025) Pemanfaatan AI dalam Keamanan Siber. Bandung: PT Sonpedia Publishing Indonesia.
Sinaga, W.M.B.B. and Firmansyah, A. (2024) ‘Perubahan paradigma pendidikan di era digital’, Jurnal Teknologi Pendidikan, 1(4), pp. 10–10.Arafat, M. and Wirasto, A.T.E. (2024) ‘Kebijakan kriminal dalam penanganan siber di era digital: Studi kasus di Indonesia’, Equal: Journal of Law and Justice, 1(2), pp. 220–241.
Syahputra, H. and Wibowo, A. (2023) ‘Comparison of support vector machine (SVM) and random forest algorithm for detection of negative content on websites’, JITEKI, 9(1), pp. 165–173.